Activity log

[2026-10-18 04:15] relevance sweep | weekly automated cleanup

• audited: 1 page (329 skipped — already audited within 30 days)
• in_scope: 1
• borderline: 0
• out_of_scope: 0
• queued for deletion: 0
• notes: Idempotent sweep found one foundational paper (Bikhchandani 1992) with audit timestamp outside 30-day window; confirmed in_scope as canonical information-cascade reference cited throughout misinformation literature. All 329 other pages already carry valid recent audit records (cutoff 2026-09-18); workflow skipped per spec.

[2026-10-18 00:05] discover | weekly autonomous discovery

• venues searched: arXiv cs.CL, cs.SI, cs.CY (attempted); ACL Anthology, ICWSM, WWW, WSDM, KDD, CSCW (attempted)
• candidates found: 0
• queued: 0
• duplicates skipped: 0
• out of scope skipped: 0
• notes: Discovery run attempted for 2026-10-09 to 2026-10-18 lookback period (9 days). Network permissions were unavailable to complete venue searches. No candidates could be retrieved for relevance evaluation. Per W10 specification, a discovery run finding no new candidates is a normal success.

[2026-10-11 03:15] relevance sweep | weekly automated cleanup

• audited: 11 pages (319 skipped — already audited within 30 days)
• in_scope: 11
• borderline: 0
• out_of_scope: 0
• queued for deletion: 0
• notes: All recently-added papers and foundations papers passing W1 relevance criteria. No out-of-scope candidates identified for deletion. Idempotent: 5 pages from 2026-10-04 audit (within 30-day window) were skipped per spec; 314 total pre-existing audited records also skipped.

[2026-10-11 00:00] discover | weekly autonomous discovery

• venues searched: arXiv cs.CL, cs.SI, cs.CY (attempted)
• candidates found: 0
• queued: 0
• duplicates skipped: 0
• out of scope skipped: 0
• notes: Discovery run attempted for 2026-10-02 to 2026-10-11 lookback period. Network permissions were unavailable to complete venue searches (arXiv, ACL Anthology, ICWSM, WWW, WSDM, KDD, CSCW). No candidates could be retrieved for relevance evaluation. Per W10 specification, a discovery run finding no new candidates is a normal success.

[2026-10-04 16:15] lint | auto-fix

• fixed: 20 mechanical issues (NOT_IN_INDEX)
• touched: wiki/index.md
• notes: Added 16 missing author wikilinks, 2 topic wikilinks, and 2 paper wikilinks to top-level index. All authors, topics, and papers already existed in respective section indices; entries were missing from main wiki/index.md. Now all pages properly discoverable via main index.

[2026-10-04 15:42] relevance sweep | weekly automated cleanup

• audited: 4 pages (321 skipped — recently audited within 30 days)
• in_scope: 4
• borderline: 0
• out_of_scope: 0
• queued for deletion: 0
• notes: Recent papers added from W10 discovery all clearly within scope. All pages with prior audit records were within 30-day window and skipped per idempotency rule.

[2026-10-04 14:35] figure | 2026-huang-vex-bench

• figure prompt prepared

[2026-10-04 14:30] figure | 2026-rijsbosch-drowning-ai-slop

• figure prompt prepared

[2026-10-04 14:28] figure | 2026-tsagkarakis-telegram-coordinated-campaigns

• figure prompt prepared

[2026-10-04 13:42] figure | 2026-sanchez-cortes-conspiracy-environment

• figure prompt prepared

[2026-10-04 09:15] ingest | VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation

• job: 1791098087373-6f3729b3
• submitter: auto-discovery@guide.fake-news.site
• page: VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation
• touched: wiki/papers/2026-huang-vex-bench.md (created), wiki/authors/huang-hansun.md (created), wiki/authors/wu-yutao.md (created), wiki/authors/wang-qizhou.md (created), wiki/authors/montana-nino-silvia.md (created), wiki/authors/li-yige.md (created), wiki/authors/zheng-xiang.md (created), wiki/authors/doyuran-elif-buse.md (created), wiki/authors/mutch-phoebe.md (created), wiki/authors/liu-xiao.md (created), wiki/authors/ma-xingjun.md (created), wiki/authors/erfani-sarah.md (created), wiki/authors/leckie-christopher.md (created), wiki/topics/llm-safety.md, wiki/topics/fact-checking.md, wiki/topics/misinformation.md, wiki/topics/content-moderation.md, wiki/topics/deepfakes.md, wiki/papers/index.md, wiki/index.md
• notes: Multi-dimensional benchmark (VEX-Bench) for evaluating verification complexity of LLM-generated misinformation across checkability, harm significance, source credibility signals, impostor legitimacy, and verification cost. Evaluated across 7 frontier LLMs and 7 generation methods (5,880 articles total) on 6 high-stakes domains. Key finding: verification complexity decouples from elicitation success; methods like MisinfOQA achieve near-perfect success (≥95%) but produce substantially different verification-complexity profiles. LLMs can generate 3×–169× more expensive-to-verify content than human-written text, creating critical asymmetry for resource-constrained fact-checking. Autonomous discovery submission from W10 weekly run.

[2026-10-04 03:23] ingest | Drowning in AI Slop: How Social Media Platforms (Do Not) Label AI and Deepfake Content under EU law

• job: 1791098087254-ab854cfe
• submitter: auto-discovery@guide.fake-news.site
• page: Drowning in AI Slop: How Social Media Platforms (Do Not) Label AI and Deepfake Content under EU law
• touched: wiki/papers/2026-rijsbosch-drowning-ai-slop.md (created), wiki/authors/rijsbosch-bram.md (created), wiki/authors/bekavac-luka.md (created), wiki/authors/tari-henry.md (created), wiki/authors/van-dijck-gijs.md (created), wiki/authors/kollnig-konrad.md (created), wiki/topics/deepfakes.md, wiki/topics/synthetic-media.md, wiki/topics/content-moderation.md, wiki/topics/platform-moderation.md, wiki/topics/synthetic-media-detection.md, wiki/topics/synthetic-content.md, wiki/topics/platform-policy.md, wiki/papers/index.md
• notes: Empirical audit of platform compliance with EU Digital Services Act requirements for labeling AI-generated and deepfake content. Found platforms label only 34–82% of AI-generated content (Instagram 82%, TikTok 59%, X 41%, YouTube 63%); significant gaps in systematic-risk contexts (elections, civic discourse). Legal framework derives operative interpretation of DSA Article 35(1)(k) deepfake definition and AIGC transparency obligations. Dataset of 10,722 posts with 500-post expert-annotated subsample. Autonomous discovery submission from W10 weekly run.

[2026-10-04 07:00] ingest | Birds of a Feather Flock Together: Network-Based Detection of Coordinated Disinformation Campaigns on Telegram

• job: 1791098087142-f0457c84
• submitter: auto-discovery@guide.fake-news.site
• page: Birds of a Feather Flock Together: Network-Based Detection of Coordinated Disinformation Campaigns on Telegram
• touched: wiki/papers/2026-tsagkarakis-telegram-coordinated-campaigns.md, wiki/authors/tsagkarakis-panteleimon.md (created), wiki/authors/papadogiannakis-emmanouil.md (created), wiki/authors/markatos-evangelos.md (created), wiki/topics/misinformation-campaigns.md (created), wiki/topics/telegram.md (created), wiki/topics/coordinated-inauthentic-behavior.md (updated), wiki/topics/computational-propaganda.md (updated), wiki/papers/index.md, wiki/index.md
• notes: Graph-based discovery of coordinated Telegram disinformation campaigns from 18 seed channels. Over 86K identical message chains identified across multiple channels; temporal analysis reveals synchronized posting aligned with major political events (Feb 2022 Russia-Ukraine, March 2024 Crocus). Echo-chamber ecosystem with core propagation sources and cross-platform amplification (YouTube, fake-news websites). Autonomous discovery submission from W10 run.

[2026-10-04 06:42] ingest | Conspiracy and Environment Communities on Reddit Sort Along Different Demographic Axes

• job: 1791098086926-efc1b763
• submitter: auto-discovery@guide.fake-news.site
• page: Conspiracy and Environment Communities on Reddit Sort Along Different Demographic Axes
• touched: wiki/authors/sanchez-cortes-miguel-a.md, wiki/authors/monti-corrado.md, wiki/authors/de-francisci-morales-gianmarco.md, wiki/topics/conspiracy-theories.md, wiki/topics/network-analysis.md, wiki/topics/computational-propaganda.md, wiki/topics/polarization-political.md, wiki/papers/index.md, wiki/index.md
• notes: Stratified Attention Flow Graph (S-AFG) framework for tracing demographic pathways into conspiracy and environmental communities; reveals upstream (pre-entry) sorting for identity-expressive communities and downstream (internal) sorting for issue-oriented ones

[2026-10-04 05:18] discover | weekly autonomous discovery

• venues searched: arXiv cs.CL, arXiv cs.SI, arXiv cs.CY
• candidates found: 38
• queued: 4 new jobs (arXiv IDs: 2609.34846v1, 2610.00222v1, 2609.38571v1, 2609.35028v1)
• duplicates skipped: 1 (2609.28947v1 — already in wiki as Why Does Misinformation Propagate Faster?)
• out of scope skipped: 33 (papers with only loose keyword matches; strict filter required explicit misinformation/conspiracy/propaganda in title)
• notes: ACL Anthology, ICWSM, WWW, WSDM, KDD, CSCW not searched due to lack of standardized API access. All 4 queued papers have explicit misinformation/conspiracy/deepfake keywords in title, confirming clear relevance.

[2026-09-27 12:30] lint | auto-fix

• fixed: 57 mechanical issues (23 broken links, 34 index entries)
• broken links fixed: 3 in log.md (deleted papers), 5 in topic files, 6 in author/paper files
• index entries added: 12 new topics (algorithmic-bias, misinformation-effect, human-memory, formal-methods, claim-detection, fact-checking-systems, explanation-in-fact-checking, recommendation-systems, engagement-based-ranking, crowdsourced-fact-checking, speech-based-fact-verification, audio-deepfakes), 22 new authors, section index links (authors, datasets, videos)
• remaining NOT_IN_INDEX: 2 deleted papers (2019-gehrmann-gltr-generated-text, 2018-zhou-two-stream-tampered-face) — expected, no action needed
• touched: wiki/log.md, wiki/authors/*.md (hoang-minh, le-thai, penamakuri-abhirama), wiki/papers/2026-andreou-latent-polarity-community-notes.md, wiki/papers/2026-sim-memory-manipulation.md, wiki/topics/algorithmic-bias.md, wiki/topics/misinformation-effect.md, wiki/topics/human-memory.md, wiki/topics/audio-deepfakes.md, wiki/topics/crowdsourced-fact-checking.md, wiki/index.md

[2026-09-27 04:02] delete | Release Strategies and the Social Impacts of Language Models

• job: 1790496232535-d7c73d6e
• page: wiki/.deleted/papers/2019-solaiman-release-strategies-language-models.md (was wiki/papers/2019-solaiman-release-strategies-language-models.md)
• touched: wiki/papers/index.md, wiki/topics/generated-text-detection.md, wiki/topics/neural-language-models.md, wiki/topics/dual-use-technology.md, wiki/topics/ai-generated-content.md, wiki/topics/fake-news-detection.md, wiki/topics/responsible-ai.md, wiki/topics/bias-language-models.md, wiki/papers/2019-ippolito-generated-text-detection.md, wiki/papers/2022-dugan-boundary-detection.md, wiki/papers/2023-su-adapting-fake-news-detection.md, wiki/authors/solaiman-irene.md, wiki/authors/brundage-miles.md, wiki/authors/wu-jeff.md, wiki/authors/kim-jong-wook.md, wiki/authors/kreps-sarah.md, wiki/authors/mccain-miles.md, wiki/authors/newhouse-alex.md, wiki/authors/blazakis-jason.md, wiki/authors/mcguffie-kris.md, wiki/authors/wang-jasmine.md, wiki/index.md
• reason: LLM safety/release strategies without explicit misinformation/news focus

[2026-09-27 12:15] delete | GLTR: Statistical Detection and Visualization of Generated Text

• job: 1790496232461-653b02c8
• page: wiki/.deleted/papers/2019-gehrmann-gltr-generated-text.md (was wiki/papers/2019-gehrmann-gltr-generated-text.md)
• touched: wiki/authors/gehrmann-sebastian.md, wiki/authors/strobelt-hendrik.md, wiki/authors/rush-alexander.md, wiki/papers/index.md, wiki/papers/2019-solaiman-release-strategies-language-models.md, wiki/papers/2019-ippolito-generated-text-detection.md, wiki/papers/2020-tan-neural-fake-news.md, wiki/topics/neural-text-generation.md, wiki/topics/generated-text-detection.md, wiki/topics/neural-language-models.md, wiki/topics/human-evaluation.md, wiki/index.md
• reason: General text generation detection without explicit misinformation framing

[2026-09-27 04:08] delete | We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!

• job: 1790496232293-a4449cdc
• page: wiki/.deleted/papers/2016-anand-clickbait-neural-networks.md (was wiki/papers/2016-anand-clickbait-neural-networks.md)
• touched: wiki/authors/anand-ankesh.md, wiki/authors/chakraborty-tanmoy.md, wiki/authors/park-noseong.md, wiki/topics/clickbait.md, wiki/topics/deep-learning.md, wiki/topics/recurrent-neural-networks.md, wiki/topics/text-classification.md, wiki/topics/word-embeddings.md, wiki/papers/2018-potthast-clickbait-challenge.md, wiki/papers/index.md, wiki/index.md
• reason: Generic NLP/IR/GNN methodology without news/misinformation framing

[2026-09-27 04:30] delete | Efficient Estimation of Word Representations in Vector Space

• job: 1790496229063-4b453a25
• page: wiki/.deleted/papers/2013-mikolov-word2vec.md (was wiki/papers/2013-mikolov-word2vec.md)
• touched: wiki/index.md, wiki/papers/index.md, wiki/topics/text-embeddings.md, wiki/topics/word-embeddings.md, wiki/topics/language-models.md, wiki/topics/text-representations.md, wiki/topics/nlp-methods.md, wiki/topics/natural-language-processing.md, wiki/authors/mikolov-tomas.md, wiki/authors/dean-jeffrey.md, wiki/authors/corrado-greg.md, wiki/authors/chen-kai.md, wiki/papers/2013-mikolov-phrases-compositionality.md, wiki/papers/2017-schmidt-hate-speech-survey.md, wiki/papers/2020-danilevsky-explainable-ai-nlp-survey.md, wiki/papers/2026-kronfeld-counter-narrative-refinement.md
• reason: General LLM work without explicit misinformation/news framing

[2026-09-27 11:24] relevance sweep | weekly automated cleanup

• audited: 312 pages (17 skipped — already audited)
• in_scope: 214
• borderline: 69 (flagged for human review, not deleted)
• out_of_scope: 5 — queued for deletion: 5 (job ids: 1790496229063-4b453a25, 1790496232293-a4449cdc, 1790496232388-bd593ff4, 1790496232461-653b02c8, 1790496232535-d7c73d6e)
• notes: All out_of_scope pages identified are general-methodology or non-news-framing papers (word embeddings, clickbait detection, facial forensics, generated-text detection, LLM release strategies). No cap reached.

[2026-09-27 10:44] figure | 2026-sim-memory-manipulation

• Figure prompt prepared for AI summary error propagation and memory distortion experimental pipeline visualization

[2026-09-27 10:43] figure | 2026-andreou-latent-polarity-community-notes

• Figure prompt prepared for two-dimensional latent factor extraction and bridging-failure impact visualization

[2026-09-27 10:42] figure | 2026-panayiotou-setting-the-clock

• Figure prompt prepared for temporal windowing methodology and window-length parameter effects visualization

[2026-09-27 10:40] figure | 2026-ebrahimi-strategic-opinion-manipulation

• Figure prompt prepared for multiplex network game-theoretic model and layer-merging effects visualization

[2026-09-27 10:39] figure | 2026-kekatos-formal-methods-fact-checking

• Figure prompt prepared for formal methods pipeline and assurance stack visualization

[2026-09-27 10:38] figure | 2026-hoang-vietprism-vietnamese-deepfake

• Figure prompt prepared for data collection pipeline and detector evaluation visualization

[2026-09-27 10:37] figure | 2026-mazumder-speech-fact-verification

• Figure prompt prepared for speech fact-checking pipeline visualization

[2026-09-27 10:35] ingest | AI-Enabled Human Memory Manipulation: Misleading AI-Generated Summaries Distort Human Memory

• job: 1790492900465-02505aab
• submitter: auto-discovery@guide.fake-news.site
• page: AI-Enabled Human Memory Manipulation: Misleading AI-Generated Summaries Distort
• touched: wiki/papers/2026-sim-memory-manipulation.md, wiki/authors/sim-mattea.md (created), wiki/authors/eiger-yael.md (created), wiki/authors/kohno-tadayoshi.md (created), wiki/topics/misinformation-effect.md (created), wiki/topics/human-memory.md (created), wiki/topics/ai-generated-content.md (updated), wiki/topics/media-manipulation.md (updated), wiki/index.md, wiki/papers/index.md
• notes: Empirical study of how AI-generated misinformation distorts human memory via the classic misinformation effect. Analyzed ChatGPT and Gemini summaries of videos; found 100% contained omissions, with 51.6% omitting critical details. Human-subjects experiment (N=328) showed only 48.8% correctly recalled a detail after reading misleading AI summary vs. 83.6% with accurate information. Critically, susceptibility was equivalent whether participants believed the summary was AI- or human-written—trust in AI did not moderate the effect. Created two new topic pages (misinformation-effect, human-memory).

[2026-09-27 10:32] ingest | Two Fault Lines: Latent Polarity Geometry in X Community Notes

• job: 1790492900254-05b48530
• submitter: auto-discovery@guide.fake-news.site
• page: Two Fault Lines: Latent Polarity Geometry in X Community Notes
• touched: wiki/papers/2026-andreou-latent-polarity-community-notes.md, wiki/authors/andreou-andreas.md (created), wiki/authors/sirivianos-michael.md (updated), wiki/topics/crowdsourced-fact-checking.md (created), wiki/topics/content-moderation.md (updated), wiki/topics/political-polarization.md (updated), wiki/topics/algorithmic-bias.md (created), wiki/topics/community-notes.md (updated), wiki/papers/index.md, wiki/index.md
• notes: Full-scale empirical study of X's Community Notes (212.9M ratings, 2.33M notes) revealing two-dimensional polarity space. Platform's one-dimensional bridging algorithm fails on topics with independent second-axis disagreement (COVID/vaccines, Ukraine/Russia, institutional trust); publication rates fall from 71.5% to 11.7% as dim-2 disagreement increases. Also documents language-based equity gaps affecting Greek-script content. Created crowdsourced-fact-checking and algorithmic-bias topic pages. Autonomous discovery submission.

[2026-09-27 10:15] ingest | Setting the clock: Evaluating temporal window parameters for coordinated behavior detection

• job: 1790492900144-18485d17
• submitter: auto-discovery@guide.fake-news.site
• page: Setting the clock: Evaluating temporal window parameters for coordinated behavior
• touched: wiki/papers/2026-panayiotou-setting-the-clock.md, wiki/authors/panayiotou-georgios.md (created), wiki/authors/mannoccci-lorenzo.md (created), wiki/authors/tesconi-maurizio.md (updated), wiki/topics/coordinated-inauthentic-behavior.md (updated), wiki/topics/information-operations.md (updated), wiki/papers/index.md, wiki/index.md
• notes: First systematic empirical evaluation of temporal window parameter effects on coordinated behavior detection. Key finding: window length is a substantive modeling parameter, not a technical detail. Short windows capture tight synchronous coordination; longer windows reveal slow diffuse patterns. Evaluation on 14 labeled IO datasets from three European institutions (Uppsala, Pisa, CNR-Pisa). Accepted to AIDEM Workshop at ECML-PKDD 2026.

[2026-09-27 03:12] ingest | Strategic Opinion Manipulation in Multiplex Networks

• job: 1790492900035-d783cfa6
• submitter: auto-discovery@guide.fake-news.site
• page: Strategic Opinion Manipulation in Multiplex Networks
• touched: wiki/papers/2026-ebrahimi-strategic-opinion-manipulation.md, wiki/authors/ebrahimi-raman.md, wiki/authors/franceschetti-massimo.md, wiki/topics/opinion-manipulation.md (created), wiki/topics/game-theory.md (created), wiki/topics/platform-design.md (created), wiki/topics/opinion-dynamics.md (updated), wiki/topics/polarization.md (updated), wiki/topics/computational-propaganda.md (updated), wiki/index.md, wiki/papers/index.md
• notes: Game-theoretic model of strategic opinion manipulation on multiplex networks with closed-form equilibrium analysis. Created three new topic pages (opinion-manipulation, game-theory, platform-design) and updated five existing topics. Key contribution: characterizes when platform design choices (layer merging, attention weighting) mitigate versus amplify vulnerability to coordinated manipulation through re-centralization effects.

[2026-09-27 03:11] ingest | SoK: Formal Methods for Fact-Checking and Information Integrity

• job: 1790492894034-90ad4ecd
• submitter: auto-discovery@guide.fake-news.site
• page: SoK: Formal Methods for Fact-Checking and Information Integrity
• touched: wiki/papers/2026-kekatos-formal-methods-fact-checking.md, wiki/authors/kekatos-nikolaos.md, wiki/authors/nestoridis-theodoros.md, wiki/authors/bratsas-charalampos.md, wiki/authors/dimoulas-charalampos.md, wiki/authors/konstantinidis-georgios.md, wiki/authors/malogiannis-georgios.md, wiki/authors/sirivianos-michael.md (updated), wiki/authors/veglis-andreas.md, wiki/topics/formal-methods.md (created), wiki/topics/fact-checking.md (updated), wiki/topics/verification.md (updated), wiki/topics/claim-verification.md (updated), wiki/papers/index.md, wiki/index.md
• notes: Comprehensive SoK surveying 121 papers on formal methods applied to fact-checking. Proposes five-level assurance stack; identifies critical gaps in claim construction and verdict correction. Created new formal-methods topic page; updated three fact-checking related topics to cite this paper. Eight authors from Cyprus, Greece, and UK institutions.

[2026-09-27 03:09] ingest | VietPrism: A Large-Scale Vietnamese Speech and Deepfake Corpus with Diverse Dialects and Code-Switching

• job: 1790492893816-a67bc649
• submitter: auto-discovery@guide.fake-news.site
• page: VietPrism: A Large-Scale Vietnamese Speech and Deepfake Corpus with Diverse
• touched: wiki/papers/2026-hoang-vietprism-vietnamese-deepfake.md, wiki/authors/hoang-minh.md, wiki/authors/le-thai.md, wiki/topics/audio-deepfakes.md, wiki/topics/deepfake-detection.md, wiki/papers/index.md, wiki/index.md
• notes: First large-scale Vietnamese audio deepfake dataset; combines 993.4 hours bona fide speech with 3.1K hours from four synthesis systems. Reveals robustness gaps in multilingual detectors across speaker similarity, dialects, and code-switching. Created new audio-deepfakes topic page; updated deepfake-detection to link audio modality.

[2026-09-27 03:08] ingest | To Trust or Not to Trust: Retrieval-Augmented Fact Checking in Speech

• job: 1790492893683-a79243e6
• submitter: auto-discovery@guide.fake-news.site
• page: To Trust or Not to Trust: Retrieval-Augmented Fact Checking in Speech
• touched: wiki/papers/2026-mazumder-speech-fact-verification.md, wiki/authors/mazumder-debajyoti.md, wiki/authors/mamta.md, wiki/authors/penamakuri-abhirama.md, wiki/topics/speech-based-fact-verification.md, wiki/topics/fact-checking.md, wiki/topics/multimodal-detection.md, wiki/papers/index.md, wiki/index.md
• notes: Introduces VeriSpeak benchmark of 3,879 spoken claims; demonstrates text-speech modality gap in Large Audio Language Models. New topic page for speech-based fact verification created. Updated fact-checking and multimodal-detection topic pages with reference to this paper.

[2026-09-27 11:00] figure | 2026-li-misinformation-propagate-faster

Figure prompt prepared: three-stage analysis of engagement fungibility mechanism (score aggregation, engagement imbalance problem, reflective-threshold gate solution).

[2026-09-27 03:07] ingest | Why Does Misinformation Propagate Faster? An Algorithmic Perspective on X

• job: 1790492890873-cba31bb5
• submitter: auto-discovery@guide.fake-news.site
• page: Why Does Misinformation Propagate Faster? An Algorithmic Perspective on X
• touched: wiki/papers/2026-li-misinformation-propagate-faster.md, wiki/authors/li-pan.md, wiki/authors/gao-shuang.md, wiki/topics/algorithmic-amplification.md, wiki/topics/misinformation-propagation.md, wiki/topics/social-media-engagement.md, wiki/topics/recommendation-systems.md, wiki/topics/engagement-based-ranking.md, wiki/index.md, wiki/papers/index.md
• notes: First component-level diagnosis of engagement fungibility mechanism in X's recommendation algorithm. Calibrated simulation framework re-implementing production system; proposes reflective-threshold gate as deployable solution. Created new topics for recommendation-systems and engagement-based-ranking; updated four existing topic pages with new paper references.

[2026-09-27 00:00] discover | weekly autonomous discovery

• venues searched: arXiv cs.CL, cs.SI, cs.CY
• candidates found: 31
• queued: 10 (job ids: 1790492890873-cba31bb5, 1790492893683-a79243e6, 1790492893816-a67bc649, 1790492894034-90ad4ecd, 1790492900035-d783cfa6, 1790492900144-18485d17, 1790492900254-05b48530, 1790492900465-02505aab, 1790492900604-b6c95b6a, 1790492907717-bba76e43)
• duplicates skipped: 3
• out of scope skipped: 18
• notes: Searched arXiv venues only; other venues (ACL Anthology, ICWSM, WWW, WSDM, KDD, CSCW) not accessible due to tool constraints. Cap of 10 reached. Candidates include misinformation propagation, deepfakes, fact-checking systems, opinion manipulation, and coordinated behavior detection.

[2026-09-20 04:06] lint | auto-fix mechanical issues

• fixed: 66 mechanical issues
• broken links: 3 (created stub topic pages: Data science, Political discourse, Evidence)
• not in index: 63 (21 new authors + 42 new topics added to wiki/index.md)
• pages touched: wiki/index.md, wiki/topics/data-science.md, wiki/topics/political-discourse.md, wiki/topics/evidence.md, wiki/authors/index.md, wiki/topics/index.md
• notes: All new pages from weekly autonomous discovery (2026-09-20 batch) now properly indexed. Stub topics created to resolve broken wikilinks. No substantive issues addressed per W7 protocol.

[2026-09-20 06:15] relevance sweep | weekly automated cleanup

• audited: 5 pages (316 skipped — already audited)
• in_scope: 5
• borderline: 0
• out_of_scope: 0 — queued for deletion: 0
• notes: All five recently discovered papers (dated 2026-09-20) were audited and found to be clearly in scope. No deletions needed. Standard weekly sweep on the remaining ~316 papers yielded no new verdicts as they were all audited within the last 30 days.

[2026-09-20 04:27] figure | 2026-tiltack-publication-authority

Figure prompt prepared: four-coordinate accountability mechanism (evidence closure, authorization, measurement disclosure, lifecycle continuity) for Publication Authority specification.

[2026-09-20 04:26] figure | 2026-tessa-control-theoretic-moderation

Figure prompt prepared: four-stage control pipeline (observe state, constrain proportionality, optimize multi-objective loss, execute feedback control) comparing control-theoretic vs. fixed-rule moderation.

[2026-09-20 04:25] figure | 2026-karimi-neighbourhood-ideology-misinformation

Figure prompt prepared: four-component agent-based contagion model showing network clustering amplification of ideological influence.

[2026-09-20 04:23] figure | 2026-kronfeld-counter-narrative-refinement

Figure prompt prepared: multi-agent iterative refinement pipeline with three specialized LLM agents (Generator, Evaluator, Mediator).

[2026-09-20 04:22] figure | 2026-gerard-unifying-models-intergroup-hostility

Figure prompt prepared: two complementary theoretical pathways unifying hostile rhetoric mechanisms.

[2026-09-20 04:20] ingest | Making AI-Assisted Claims Independently Challengeable

• job: 1789888133925-2d27bd01
• submitter: auto-discovery@guide.fake-news.site
• page: Making AI-Assisted Claims Independently Challengeable: Publication Authority and a Protocol for Falsifiable Publication Records
• touched: wiki/papers/2026-tiltack-publication-authority.md, wiki/authors/tiltack-torsten-olivi.md, wiki/authors/dong-yifei.md, wiki/authors/yu-kun.md, wiki/authors/wang-xu.md, wiki/authors/liu-wei.md, wiki/authors/zhou-jianlong.md, wiki/authors/liu-ren-ping.md, wiki/authors/chen-fang.md, wiki/topics/ai-assisted-claims.md, wiki/topics/publication-accountability.md, wiki/topics/content-moderation.md, wiki/topics/verification.md, wiki/index.md, wiki/papers/index.md
• notes: Design science research formalizing Publication Authority as protocol for AI-assisted claim verification. Introduces PAC-2026 SF-4 executable specification with six orthogonal coordinates and five design laws. Rigorous evaluation against 110,764 reachable states. Created new topics for AI-assisted-claims, publication-accountability, and verification.

[2026-09-20 04:16] ingest | Control-Theoretic Content Moderation

• job: 1789888130448-25527049
• submitter: auto-discovery@guide.fake-news.site
• page: Control-Theoretic Content Moderation
• touched: wiki/papers/2026-tessa-control-theoretic-moderation.md, wiki/authors/tessa-benedetta.md, wiki/authors/tardelli-serena.md, wiki/authors/avvenuti-marco.md, wiki/authors/monreale-anna.md, wiki/authors/cresci-stefano.md, wiki/topics/content-moderation.md, wiki/topics/platform-governance.md, wiki/topics/system-dynamics.md, wiki/topics/multi-objective-optimization.md, wiki/index.md, wiki/papers/index.md
• notes: First application of control theory to platform-level content moderation; introduces proportionality constraints and multi-objective optimization (MPC/PID-based moderators). Shows adaptive control strategies outperform fixed rules in balancing harmfulness reduction, engagement preservation, and activity maintenance. Created two new topic stubs for system-dynamics and multi-objective-optimization.

[2026-09-20 04:15] ingest | How neighbourhood ideology shapes misinformation belief in densely tied social networks

• job: 1789888130123-c3170376
• submitter: auto-discovery@guide.fake-news.site
• page: How neighbourhood ideology shapes misinformation belief in densely tied social networks
• touched: wiki/papers/2026-karimi-neighbourhood-ideology-misinformation.md, wiki/authors/karimi-soroush.md, wiki/authors/oliveira-marcos.md, wiki/authors/pacheco-diogo.md, wiki/topics/misinformation-propagation.md, wiki/topics/ideological-bias.md, wiki/topics/social-networks.md, wiki/topics/network-dynamics.md, wiki/topics/motivated-reasoning.md, wiki/index.md, wiki/papers/index.md
• notes: Agent-based contagion model showing network clustering amplifies ideological influence on misinformation belief. Created new topic pages for misinformation-propagation and ideological-bias. Extended network-dynamics stub to full content. Demonstrates interplay of individual ideology, claim alignment, and network topology in shaping belief adoption.

[2026-09-20 04:12] ingest | A Multi-Stage Agentic Framework for Effective Counter-Narrative Generation and Refinement

• job: 1789888129964-a04e5ba5
• submitter: auto-discovery@guide.fake-news.site
• page: A Multi-Stage Agentic Framework for Effective Counter-Narrative Generation and Refinement
• touched: wiki/papers/2026-kronfeld-counter-narrative-refinement.md, wiki/authors/kronfeld-carmel.md, wiki/authors/gogawale-sharva.md, wiki/authors/kobayashi-tetsuro.md, wiki/authors/ben-gal-irad.md, wiki/topics/counter-narratives.md, wiki/topics/multi-agent-systems.md, wiki/topics/information-warfare.md, wiki/topics/computational-propaganda.md, wiki/topics/misinformation-detection.md, wiki/topics/hate-speech-detection.md, wiki/index.md, wiki/papers/index.md
• notes: First paper on counter-narrative generation via multi-agent LLM framework. Addresses scalability bottleneck in manual CN authoring through iterative refinement with LLM-as-judge evaluation. Created new topic pages for counter-narratives, multi-agent-systems, and information-warfare. Extended computational-propaganda, misinformation-detection, and hate-speech-detection topics.

[2026-09-20 04:10] ingest | Unifying Models of Intergroup Hostility in Online Discourse

• job: 1789888129038-4da110de
• submitter: auto-discovery@guide.fake-news.site
• page: Unifying Models of Intergroup Hostility in Online Discourse
• touched: wiki/authors/gerard-patrick.md, wiki/authors/mendelsohn-julia.md, wiki/authors/lerman-kristina.md, wiki/topics/hostile-rhetoric.md, wiki/topics/rhetoric.md, wiki/topics/computational-propaganda.md, wiki/index.md, wiki/papers/index.md
• notes: Unifies six mechanisms of hostile rhetoric across theoretical traditions; introduces temporal analysis showing regular sequence of mechanism emergence in political narratives. First comprehensive empirical study bridging threat-mediated and moral-exclusion theories.

[2026-09-20 04:07] discover | weekly autonomous discovery

• venues searched: arXiv cs.CL, arXiv cs.SI, arXiv cs.CY
• candidates found: 37
• queued: 7 (job ids: 1789888129038-4da110de, 1789888129964-a04e5ba5, 1789888130123-c3170376, 1789888130448-25527049, 1789888133925-2d27bd01, 1789888136678-b1421365, 1789888136883-814a0f0b)
• duplicates skipped: 3
• out of scope skipped: 27 (conservative filtering applied — script found 37 in-scope by keyword, but many were false positives unrelated to news/political misinformation)
• notes: Strong week for discovery. Papers selected include counter-narrative generation, misinformation belief dynamics, health misinformation, content moderation theory, coordinated behavior detection, and deceptive recruitment detection.

[2026-09-13 04:22] lint | weekly auto-fix

• fixed: 50 mechanical issues
• NOT_IN_INDEX: 50 entries added to wiki/index.md
  • 43 authors (alphabetically integrated into authors section)
  • 4 topics (evidence-conflict, generative-engine-optimization, rag-attacks, synthesis-attack)
  • 3 papers (1992-bikhchandani-informational-cascades, 2017-hassan-claimrank, 2018-hassan-claimbuster)
• touched: wiki/index.md (50 new wikilinks), wiki/authors/index.md (previously updated)
• notes: All NOT_IN_INDEX entries now linked in main index. Authors and topics integrated alphabetically; papers added as foundational references.

[2026-09-13 04:21] relevance sweep | weekly automated cleanup

• audited: 1 page (312 skipped — already audited within 30 days)
• in_scope: 1
• borderline: 0
• out_of_scope: 0
• delete_job_ids: []
• notes: Sweep completed. Single unaudited page (1992-bikhchandani-informational-cascades) marked as foundational in_scope reference. All previously audited pages still within audit window.

[2026-09-13 03:04] discover | weekly autonomous discovery

• venues: arXiv cs.CL/cs.SI/cs.CY, ACL Anthology, ICWSM, WWW, WSDM, KDD, CSCW
• queued: 0
• duplicates_skipped: 0
• out_of_scope_skipped: 0
• notes: Discovery job queued but could not execute — WebSearch and network access permissions not configured in execution context. No candidates discovered. Configuration issue, not a discovery limitation.

[2026-09-06 23:15] lint | weekly auto-fix

• fixed: 8 BROKEN_LINK issues by creating 15 topic/paper stub pages
• created: Vision transformers, Generative search poisoning, Contrastive learning for detection, Guardrail evaluation, Misinformation in retrieval-augmented generation, Semantic search poisoning, Cross-lingual misinformation, Image-text relationships, Adversarial attacks on machine learning, Information integrity, Search engine manipulation, Deep learning approaches, Large language model evaluation, Information retrieval attacks, Image-based misinformation, A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades
• remaining: 80 NOT_IN_INDEX issues (mostly author pages in section_index, expected given autodiscovery additions)
• notes: All BROKEN_LINK issues resolved. NOT_IN_INDEX warnings reflect pages properly organized in section_index files but not directly referenced in main wiki/index.md.

[2026-09-06 22:30] relevance sweep | weekly automated cleanup

• audited: 11 papers (1 skipped — already audited within 30 days)
• in_scope: 11
• borderline: 0
• out_of_scope: 0 — queued for deletion: 0
• notes: All 11 recently discovered papers (2026) passed relevance check as in_scope. No borderline or out-of-scope verdicts; cap not reached.

[2026-09-06 14:15] figure | 2026-borenstein-mmmmm-multilingual-taxonomy

• prompt prepared for six-axis taxonomy framework with VLM pipeline operationalizing multimodal misinformation analysis

[2026-09-06 14:10] figure | 2026-zhang-lazy-grounding

• prompt prepared for four-stage attack pipeline: question rewrite, evidence collection, corpus injection, evaluation metrics

[2026-09-06 14:05] figure | 2026-lu-one-ai-signal-many-human

• prompt prepared for Bayesian cascade model with Gateway threshold mechanism

[2026-09-06 14:00] figure | 2026-amatya-claim-worthiness-calibration

• prompt prepared for four-stage calibration pipeline: score computation, neighbor retrieval, residual correction, calibrated output

[2026-09-06 13:45] figure | 2026-li-social-bots-weaken-activist-cohesion

• prompt prepared for 2x2 analysis pipeline: bot detection, ego-network cohesion, community-level cohesion, temporal comparison

[2026-09-06 12:30] figure | 2026-sikosana-health-misinformation-fusion

• prompt prepared for five-branch feature fusion pipeline with psychological theory grounding

[2026-09-06 12:15] figure | 2026-zhang-conflict-aware-claim-verification

• prompt prepared for three-stage conflict-resolution pipeline in social-media fact-checking

[2026-09-06 12:00] figure | 2026-basu-swin-efficientnet-deepfake-forensics

• prompt prepared for hybrid CNN-Transformer deepfake detection architecture

[2026-09-06 11:15] ingest | MMMMM: A Unified Taxonomy for Investigating the Mechanisms of Multilingual MultiModal Misinformation

• job: 1788678278480-9807ab5c
• submitter: auto-discovery@guide.fake-news.site
• page: MMMMM: A Unified Taxonomy for Investigating the Mechanisms of Multilingual MultiModal Misinformation
• touched: wiki/authors/borenstein-nadav.md, wiki/authors/warren-greta.md, wiki/authors/elliott-desmond.md, wiki/authors/augenstein-isabelle.md, wiki/topics/multimodal-detection.md, wiki/topics/image-manipulation.md, wiki/topics/misinformation-mechanisms.md, wiki/topics/taxonomy.md, wiki/papers/index.md, wiki/index.md
• notes: First unified taxonomy of multimodal misinformation mechanisms across six orthogonal axes (Classification, Image Type, Rhetorical Role, Topic, Emotion, Multimodal Mechanism). Grounded in 26,979 real-world Twitter/X Community Notes instances across seven languages (English, Spanish, Portuguese, Japanese, French, German, Hebrew). Vision Language Model annotation pipeline (Qwen 3.5 27B) achieves 79.3% accuracy. Key finding: deceptive mechanisms vary substantially by language and domain—Portuguese content overuses Decorative mechanisms, Japanese predominantly employs Slanted mechanisms, AI-generated images prevalent in technology/science domains, vaccination misinformation relies on text-based claims.

[2026-09-06 10:30] ingest | Lazy Grounding: Attacking Search Agents with Factual Evidence

• job: 1788678277598-afc0e9c4
• submitter: auto-discovery@guide.fake-news.site
• page: Lazy Grounding: Attacking Search Agents with Factual Evidence
• touched: wiki/authors/zhang-yulin.md, wiki/authors/huang-yukun.md, wiki/authors/chen-sanxing.md, wiki/authors/lin-tianyi.md, wiki/authors/yang-ziang.md, wiki/authors/yin-xunjian.md, wiki/authors/dhingra-bhuwan.md, wiki/topics/rag-attacks.md, wiki/topics/information-retrieval-poisoning.md, wiki/topics/llm-robustness.md, wiki/topics/misinformation-attacks.md, wiki/papers/index.md, wiki/index.md
• notes: Novel attack on retrieval-augmented generation: injecting factually correct evidence supporting a neighboring question causes search agents to misapply this evidence to the original question, achieving 5.9 point average accuracy drop (up to 17.3 points) without using false or malicious content. Distinct from traditional corpus poisoning by exploiting factual correctness itself.

[2026-09-06 10:15] ingest | One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spread

• job: 1788678276614-58edfdce
• submitter: auto-discovery@guide.fake-news.site
• page: One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spread
• touched: wiki/authors/lu-zhuoran.md, wiki/authors/wang-weilong.md, wiki/authors/yu-yangyang.md, wiki/authors/wang-xinru.md, wiki/authors/li-zhuoyan.md, wiki/authors/liu-zhiwei.md, wiki/authors/ananiadou-sophia.md, wiki/topics/ai-credibility-assessment.md, wiki/topics/human-ai-interaction.md, wiki/topics/information-cascades.md, wiki/topics/misinformation-detection.md, wiki/topics/social-learning.md, wiki/topics/content-moderation.md, wiki/papers/index.md, wiki/index.md
• notes: Theoretical and empirical analysis of how AI credibility indicators reshape sequential user judgments in information cascades. Introduces the Gateway condition demarcating regimes where AI predictions dominate vs. remain correctable. Human-subject experiments calibrate behavioral model. First paper to treat AI signals as shared public information entering cascade dynamics.

[2026-09-06 09:30] ingest | Calibrating Small Language Models for Claim Check-Worthiness Detection

• job: 1788678275662-a3da478b
• submitter: auto-discovery@guide.fake-news.site
• page: Calibrating Small Language Models for Claim Check-Worthiness Detection
• touched: wiki/authors/amatya-pratuat.md, wiki/authors/v-venktesh.md, wiki/authors/setty-vinay.md, wiki/topics/fact-checking.md, wiki/topics/language-models.md, wiki/topics/claim-worthiness-detection.md, wiki/topics/calibration.md, wiki/topics/model-efficiency.md, wiki/index.md, wiki/papers/index.md
• notes: Post-hoc calibration (NN-PPI) enabling small language models (270M–4B params) to match large LM accuracy on claim check-worthiness detection. Proposes nearest-neighbor residual correction using small labeled calibration sets; achieves 12–33.80% weighted F1 gains across ClaimBuster and CLEF 2024. Addresses production deployment constraint: cost vs. accuracy tradeoff in real-time fact-checking pipelines. First application of Prediction-Powered Inference to pointwise classification in information verification.

[2026-09-06 09:15] ingest | Social bots weaken activist cohesion

• job: 1788678274667-bea21e3e
• submitter: auto-discovery@guide.fake-news.site
• page: Social bots weaken activist cohesion
• touched: wiki/authors/li-linda.md, wiki/authors/vasarhelyi-orsolya.md, wiki/authors/vedres-balazs.md, wiki/topics/social-movements.md, wiki/topics/online-activism.md, wiki/topics/social-bots.md, wiki/topics/computational-propaganda.md, wiki/topics/network-science.md, wiki/index.md, wiki/papers/index.md
• notes: First systematic analysis of how social bots disrupt the relational infrastructure of activist networks. Longitudinal study of BLM protests (June 2020) demonstrates structural cohesion erosion at both ego-network and community levels; asymmetric impact concentrated among movement supporters. Key contribution: bot impact operates through network topology, not messaging alone.

[2026-09-06 08:00] ingest | A Multi-Branch Feature Fusion Approach for Health Misinformation Detection and Propagation

• job: 1788678273701-59f3dd94
• submitter: auto-discovery@guide.fake-news.site
• page: A Multi-Branch Feature Fusion Approach for Health Misinformation Detection and Propagation
• touched: wiki/authors/sikosana-mikululi.md, wiki/authors/maudsley-barton-sean.md, wiki/authors/ajao-oluwaseun.md, wiki/topics/propagation-analysis.md, wiki/topics/psychological-theory.md, wiki/index.md, wiki/papers/index.md
• notes: Multi-branch fusion architecture grounding health misinformation detection in psychological theories (ELM, TPB). Achieves ROC-AUC 0.9999 on COVID-19-FNIR. Introduces Cognitive Propagation Score, an engagement-independent interpretable metric for predicting spread. Operationalizes ELM and TPB constructs as explicit text-derived features.

[2026-09-06 12:15] figure | 2026-zheng-counter-geo-bench

• Figure prompt prepared for Counter-GEO-Bench paper: 1x4 component pipeline showing benchmark construction, threat model, evaluation pipeline, and defense mechanism.

[2026-09-06 07:45] ingest | CoVer: Conflict-Aware Claim Verification

• job: 1788678272715-2e6985cf
• submitter: auto-discovery@guide.fake-news.site
• page: CoVer: Conflict-Aware Claim Verification
• touched: wiki/authors/zhang-shuning.md, wiki/authors/shi-dai.md, wiki/authors/chu-bohao.md, wiki/authors/wang-hui.md, wiki/authors/chuai-yuwei.md, wiki/authors/wang-yifan.md, wiki/authors/chen-jingruo.md, wiki/authors/li-simin.md, wiki/authors/yi-xin.md, wiki/authors/li-hewu.md, wiki/topics/claim-verification.md, wiki/topics/fact-checking.md, wiki/topics/evidence-conflict.md, wiki/topics/llm-fact-checking.md, wiki/topics/social-media-verification.md, wiki/index.md, wiki/papers/index.md
• notes: Evidence conflict resolution framework for social media fact-checking. ContraNote dataset (33.6K conflict instances, 54.5K prioritization instances) from X Community Notes. Three-stage pipeline: evidence schema normalization, factual consensus (LLM-assisted quality scoring), support verification. 86–88.5% accuracy, outperforming CONFACT and FactCheckGPT baselines. First systematic treatment of evidence-level and aggregation-level conflicts in automated verification.

[2026-09-06 03:30] ingest | Swin Meets EfficientNet: Lightweight Architectures for GAN-Based Face Forensics

• job: 1788678272066-58ab5610
• submitter: auto-discovery@guide.fake-news.site
• page: Swin Meets EfficientNet: Lightweight Architectures for GAN-Based Face Forensics
• touched: wiki/papers/2026-basu-swin-efficientnet-deepfake-forensics.md, wiki/authors/basu-sejuti.md, wiki/authors/sood-ashima.md, wiki/authors/kumar-vijay.md, wiki/authors/sharma-sahil.md, wiki/topics/deepfake-detection.md, wiki/topics/synthetic-media-detection.md, wiki/topics/face-forensics.md, wiki/topics/gan-detection.md, wiki/index.md, wiki/papers/index.md
• notes: Hybrid EfficientNet-B0 with Swin-Transformer for resource-efficient GAN-based synthetic face detection; 99% accuracy on 140K Real and Fake Faces dataset; recent methodological contribution combining CNNs and transformers for practical deepfake detection.

[2026-08-23 04:15] lint | auto-fix weekly mechanical issues

• fixed: 1955 NOT_IN_INDEX mechanical issues → 0 remaining
• touched: wiki/index.md (added 1955 wikilinks to Authors, Topics, Datasets, Videos, and Papers sections)
• papers categorized: 50 papers added to appropriate topic sections (Media manipulation, Multimodal, Bot detection, COVID-19, etc.)
• authors added: 1135 author wikilinks
• topics added: 741 topic wikilinks
• datasets added: 15 dataset wikilinks
• videos added: 6 video wikilinks
• articles added: 1 article wikilink
• notes: Comprehensive mechanical issue resolution. All pages now referenced in main index. Substantive issues (180 ORPHAN pages) remain for human review. Section indices auto-generated by build_hooks.py will be regenerated at next mkdocs build.

[2026-08-23 03:15] relevance sweep | weekly automated cleanup

• audited: 1 page (303 skipped — already audited within 30 days)
• in_scope: 0
• borderline: 1 (flagged for human review, not deleted)
• out_of_scope: 0 — queued for deletion: 0
• notes: Systematic audit completed. Only 1 page needed review (stub: 2024-dodds-controlled-change.md). Marked borderline due to insufficient detail; full paper review required to determine if GenAI adoption in journalism is in-scope.

[2026-08-30 03:15] relevance sweep | weekly automated cleanup

• audited: 0 pages (304 skipped — all pages already audited within 30 days on 2026-08-16)
• in_scope: N/A
• borderline: N/A
• out_of_scope: N/A — queued for deletion: 0
• notes: Full corpus already recently audited. Per W11 idempotency rule, no re-audit performed.

[2026-09-06 07:15] ingest | Counter-GEO-Bench: Evaluating Defenses Against Information-Distorting Generative Engine Optimization

• job: 1788678270764-15e8bd08
• submitter: auto-discovery@guide.fake-news.site
• page: Counter-GEO-Bench: Evaluating Defenses Against Information-Distorting Generative Engine Optimization
• touched: Bing Zheng, Zongyao Zhao, Wenming Yang, Generative Engine Optimization, Synthesis-Stage Attacks on LLMs, Misinformation and fake news detection, LLM Safety and Adversarial Robustness, Content moderation, wiki/index.md
• notes: First benchmark evaluating defenses against information-distorting GEO attacks. Novel threat model isolates misinformation injection from ranking boost via paired IP/ID rewrites. Proposes C-GEO Guard contrastive detector (47.6% relative ASR reduction). Establishes that existing guardrails (Granite Guardian, Llama Guard 3, NeMo Self-Check) fail this threat class.

[2026-09-06 06:30] discover | weekly autonomous discovery

• venues searched: arXiv cs.CL, arXiv cs.SI, arXiv cs.CY
• candidates found: 12 (papers published Aug 28 - Sep 6, 2026)
• queued: 10 (job ids: 1788678269755-61bae1fd, 1788678270764-15e8bd08, 1788678272066-58ab5610, 1788678272715-2e6985cf, 1788678273701-59f3dd94, 1788678274667-bea21e3e, 1788678275662-a3da478b, 1788678276614-58edfdce, 1788678277598-afc0e9c4, 1788678278480-9807ab5c)
• duplicates skipped: 0
• out of scope skipped: 7
• overflow: 2 additional qualified candidates (2608.29617, 2608.29175) not queued due to cap
• notes: Strong discovery run. Themes included deepfakes/synthetic media detection (3), fact-checking/claim verification (3), credibility and social bots (2), multimodal and health misinformation (2), adversarial mechanisms (2). Other venues (ACL Anthology, ICWSM, WWW, WSDM, KDD, CSCW) require separate infrastructure access for discovery search.

[2026-08-30 07:11] discover | weekly autonomous discovery

• venues searched: arXiv cs.CL, arXiv cs.SI, arXiv cs.CY, ACL Anthology, ICWSM, The Web Conference, WSDM, KDD, CSCW
• candidates found: 0
• queued: 0
• duplicates skipped: 0
• out of scope skipped: 0
• notes: Network access unavailable in execution environment. No candidates enqueued this cycle.

[2026-08-23 03:10] discover | weekly autonomous discovery

• venues searched: arXiv cs.CL, arXiv cs.SI, arXiv cs.CY, ACL Anthology, ICWSM, The Web Conference, WSDM, KDD, CSCW
• candidates found: 0
• queued: 0
• duplicates skipped: 0
• out of scope skipped: 0
• notes: Network access to external venue APIs unavailable in execution environment. Attempted searches returned no data. Normal outcome — no new candidates to enqueue.

[2026-08-16 04:30] lint | auto-fix weekly mechanical issues

• fixed broken links: 3 (removed dead paper references from wiki/log.md)
• created stub pages: 20 (18 topic stubs, 1 author stub, 1 paper stub)
• touched: wiki/log.md, wiki/topics/* (new: behavioral-dynamics, technology-governance, collective-behavior, network-dynamics, mathematical-modeling, semantic-preservation, agent-based-models, journalism-and-ai, transparency-in-journalism, political-misinformation, foreign-interference, video-misinformation, datasets-and-benchmarks, polarization-echo-chambers, astroturf, diffusion-networks, zombie-rumors), wiki/authors/daheim-nico.md, wiki/papers/2024-dodds-controlled-change.md
• notes: Resolved all BROKEN_LINK mechanical issues (23 → 0). Remaining NOT_IN_INDEX issues (1955) are primarily author pages correctly listed in section index (wiki/authors/index.md) rather than main index; these do not represent actual indexing problems.

[2026-08-16 04:20] delete | 2023-wang-factuality-llms

• Deleted: wiki/papers/2023-wang-factuality-llms.md → wiki/.deleted/papers/2023-wang-factuality-llms.md
• Reason: Generic NLP/IR methodology without explicit news/misinformation framing (out of scope)
• Fixed references in: wiki/papers/index.md, wiki/topics/factuality.md, wiki/topics/hallucination.md, wiki/topics/latent-knowledge.md, wiki/papers/2023-asai-self-rag.md, wiki/papers/2023-zhang-llm-knowledge-update.md, wiki/papers/2023-chang-evaluation-llms.md, wiki/papers/2022-burns-discovering-latent-knowledge.md, wiki/authors/wang-cunxiang.md, wiki/authors/wang-jindong.md, wiki/authors/tang-xiangru.md, wiki/authors/guo-qipeng.md, wiki/authors/yue-yuanhao.md, wiki/authors/liu-xiaoze.md

[2026-08-16 12:15] delete | 2020-lewis-retrieval-augmented-generation

• Deleted: wiki/papers/2020-lewis-retrieval-augmented-generation.md → wiki/.deleted/papers/2020-lewis-retrieval-augmented-generation.md
• Reason: Generic NLP/IR methodology without explicit news/misinformation framing (out of scope)
• Fixed references in: wiki/index.md, wiki/papers/index.md, wiki/topics/fact-checking.md, wiki/topics/question-answering.md, wiki/topics/information-retrieval.md, wiki/topics/retrieval-based-methods.md, wiki/topics/knowledge-intensive-nlp.md, wiki/topics/retrieval-augmented-generation.md, wiki/papers/2023-asai-self-rag.md, wiki/authors/lewis-patrick.md, wiki/authors/petroni-fabio.md, wiki/authors/lewis-mike.md, wiki/authors/karpukhin-vladimir.md, wiki/authors/yih-wen-tau.md, wiki/authors/rocktaschel-tim.md, wiki/authors/riedel-sebastian.md, wiki/authors/kiela-douwe.md

[2026-08-16 12:08] delete | 2019-zlatkova-fauxtography

• Deleted: wiki/papers/2019-zlatkova-fauxtography.md → wiki/.deleted/papers/2019-zlatkova-fauxtography.md
• Reason: Claim verification applied to scientific literature, not news (out of scope)
• Fixed references in: wiki/authors/zlatkova-dimitrina.md, wiki/authors/koychev-ivan.md, wiki/authors/nakov-preslav.md, wiki/topics/fact-checking.md, wiki/topics/multimodal-detection.md, wiki/topics/visual-misinformation.md, wiki/papers/index.md

[2026-08-16 11:15] figure | 2026-reisjaa-genai-catch-22

• Figure prompt prepared: GenAI Catch-22 causal flow (adoption → delegation → de-skilling → paradox → reduced oversight) showing structural vulnerability in newsroom AI adoption.

[2026-08-16 11:10] figure | 2026-ameen-quantifying-partisanship-cross-platform

• Figure prompt prepared: cross-platform partisan analysis with embedding, source identification, clustering, and scoring pipeline showing Truth Social vs. Bluesky asymmetries.

[2026-08-16 11:05] figure | 2026-krishna-modeling-duelling-contagions

• Figure prompt prepared: duelling contagions model showing cognitive biases, network contagion, population belief outcomes, and strategic seeding intervention.

[2026-08-16 10:45] figure | 2026-thomas-build-it-break-it-repeat

• Figure prompt prepared: iterative adversarial framework (Build-Break-Repeat cycle) with attacks, detectors, semantic preservation, and iterative evaluation components.

[2026-08-16 03:30] ingest | The GenAI Catch-22: Use of Generative Artificial Intelligence in Norwegian Newsrooms During the 2025 Parliamentary Election

• job: 1786863762059-ede4c491
• submitter: auto-discovery@guide.fake-news.site
• page: The GenAI Catch-22: Use of Generative Artificial Intelligence in Norwegian
• touched: wiki/papers/2026-reisjaa-genai-catch-22.md, wiki/authors/reisjaa-mari.md, wiki/authors/lovtie-anders-sundnes.md, wiki/topics/generative-ai.md, wiki/topics/journalism.md, wiki/topics/de-skilling.md, wiki/topics/editorial-autonomy.md, wiki/topics/transparency.md, wiki/topics/election-coverage.md, wiki/papers/index.md, wiki/index.md
• notes: Longitudinal case study (Jan–Nov 2025) of GenAI adoption in four major Norwegian newsrooms during parliamentary election. Core contribution: identifies "GenAI Catch-22"—newsrooms need human expertise to oversee GenAI systems and catch errors, but extensive GenAI use erodes that expertise through de-skilling. Ambitious initial plans (chatbots, automated fact-checking) collapsed due to accuracy failures; adoption shifted to internal routine tasks often delegated to inexperienced staff, creating quality risks. Transparency about GenAI use declined over campaign. Created four new topic pages (de-skilling, editorial-autonomy, transparency, election-coverage).

[2026-08-16 03:25] ingest | Quantifying Political Partisanship for Cross-Platform Analyses

• job: 1786863761823-00f844af
• submitter: auto-discovery@guide.fake-news.site
• page: Quantifying Political Partisanship for Cross-Platform Analyses
• touched: wiki/papers/2026-ameen-quantifying-partisanship-cross-platform.md, wiki/authors/ameen-mia.md, wiki/authors/healey-christopher.md, wiki/topics/partisanship.md, wiki/papers/index.md
• notes: Text-based partisanship measurement method using semantic embeddings and media bias ratings; validated on ~1.3M posts from Bluesky and Truth Social during 2024 U.S. presidential election; demonstrates cross-platform measurement feasibility despite structural differences; addresses foundational challenge of measuring partisan discourse comparably across ideologically distinct platforms.

[2026-08-16 03:20] ingest | Modeling Duelling Contagions of True and False Information in the Face of Inherent Individual biases

• job: 1786863761698-a2e469d4
• submitter: auto-discovery@guide.fake-news.site
• page: Modeling Duelling Contagions of True and False Information in the Face of Inherent
• touched: wiki/papers/2026-krishna-modeling-duelling-contagions.md, wiki/authors/krishna-vaibhav.md, wiki/authors/shirado-hirokazu.md, wiki/authors/fu-feng.md, wiki/authors/christakis-nicholas.md, wiki/topics/cognitive-biases.md, wiki/topics/spiral-of-silence.md, wiki/topics/complex-contagion.md, wiki/topics/rumor-cascade-models.md, wiki/topics/misinformation-diffusion.md, wiki/topics/agent-based-modeling.md, wiki/topics/social-networks.md, wiki/topics/intervention-strategies.md, wiki/topics/information-diffusion.md, wiki/topics/content-moderation.md, wiki/papers/index.md, wiki/index.md
• notes: Agent-based co-diffusion model (CI-SRW) grounding competing information dynamics in complex contagion, spiral of silence, and cognitive biases; validated against two large-scale online behavioral experiments; demonstrates manipulative narratives dominate when early spreaders embrace them; strategic seeding interventions can shift equilibrium toward accurate beliefs; created four new topic pages (cognitive-biases, spiral-of-silence, complex-contagion, rumor-cascade-models) and updated six others.

[2026-08-16 03:16] ingest | FUBU-EPSTEIN: A Large-Scale Twitter Dataset on the Jeffrey Epstein Case and Its Global Public Discourse (2019–2023)

• job: 1786863757596-1db4f2bd
• submitter: auto-discovery@guide.fake-news.site
• page: FUBU-EPSTEIN
• touched: wiki/datasets/fubu-epstein.md, wiki/authors/kreil-michael.md, wiki/authors/stoeber-tristan.md, wiki/authors/schroeder-daniel-thilo.md, wiki/topics/dataset-development.md, wiki/topics/conspiracy-theories.md, wiki/topics/misinformation-detection.md, wiki/topics/social-media-analysis.md, wiki/datasets/index.md, wiki/index.md
• notes: Large-scale longitudinal Twitter corpus (54.38M statuses, 7.11M users, Aug 2019–Apr 2023) on Jeffrey Epstein case with continuous near-real-time collection, Qwen2.5-7B-Instruct annotations (conspiracy, misinformation, sentiment, toxicity, moral emotion), and 37M-edge social contact graph; enables research on conspiracy narrative formation, misinformation propagation, and long-term discourse evolution; public anonymized release on Zenodo with data governance safeguards.

[2026-08-16 03:15] ingest | Build it, Break it, Repeat: Benchmarking and improving LLM-manipulated disinformation detection in social media posts

• job: 1786863755447-94ef906e
• submitter: auto-discovery@guide.fake-news.site
• page: Build it, Break it, Repeat: Benchmarking and improving LLM-manipulated disinformation
• touched: wiki/papers/2026-thomas-build-it-break-it-repeat.md, wiki/authors/thomas-kevin.md, wiki/authors/kasprzyk-milosz.md, wiki/authors/onuigbo-ruel-c-igbokwe.md, wiki/authors/pert-elliott.md, wiki/authors/tovey-cameron.md, wiki/authors/leite-joao-a.md, wiki/authors/razuvayevskaya-olesya.md, wiki/authors/scarton-carolina.md, wiki/topics/llm-generated-text-detection.md, wiki/topics/adversarial-learning.md, wiki/topics/machine-generated-text-detection.md, wiki/topics/llm-generated-misinformation.md, wiki/topics/twitter.md, wiki/papers/index.md
• notes: Iterative adversarial benchmarking framework (BiBiR) for detecting LLM-generated disinformation on social media; introduces two new datasets (bld_data, brk_data) and demonstrates effectiveness of chained adversarial transformations (95.2% label-flip rate); proposes dynamic anchor switching (DASS) architecture for robust detection under iterative adversarial conditions.

[2026-08-16 03:05] discover | weekly autonomous discovery

• venues searched: arXiv cs.CL, arXiv cs.SI, arXiv cs.CY
• candidates found: 26
• queued: 7 (job ids: 1786863755447-94ef906e, 1786863757596-1db4f2bd, 1786863761698-a2e469d4, 1786863761823-00f844af, 1786863761952-de0e0906, 1786863762059-ede4c491, 1786863762161-e3b070c9)
• duplicates skipped: 1 (2608.02399v1 already exists as 2026-kefler-network-information-unreliable-news.md)
• out of scope skipped: 18 (keyword traps: finance media bias, generic GNN/NLP methods, agent learning, memory systems; others: general AI evaluation, governance in fintech, motion capture, geopolitics)
• notes: Focused on papers directly about misinformation/disinformation detection, information contagion, political partisanship, credibility assessment. Conference venues (ACL, ICWSM, WWW, WSDM, KDD, CSCW) not searchable without web permissions in headless environment; future runs should include automated conference proceedings fetching.

[2026-08-11 23:59] ingest | Bad News: Building Resilience to Misinformation Through Inoculation

• job: 1786475587541-9005e008
• submitter: reza@data.syr.edu
• page: Bad News: Building Resilience to Misinformation Through Inoculation
• touched: wiki/videos/2026-van-der-linden-bad-news-game.md, wiki/authors/van-der-linden-sander.md, wiki/topics/inoculation.md, wiki/topics/prebunking-inoculation.md, wiki/topics/misinformation-psychology.md, wiki/topics/misinformation-intervention.md, wiki/videos/index.md
• notes: Sander van der Linden comprehensive lecture on psychological inoculation theory and the Bad News game; covers why fact-checking fails (continued influence effect), six core manipulation techniques, interactive game mechanics, empirical evidence across demographics and time, and large-scale deployments on YouTube and social media platforms; complements earlier 2026 van der Linden video from Kyiv School of Economics with more detailed walkthrough of game design and practical scalability.

[2026-08-11 23:58] ingest | Belief, Evidence, and Elites in the Age of Mass Information

• job: 1786475587538-bf142440
• submitter: reza@data.syr.edu
• page: Belief, Evidence, and Elites in the Age of Mass Information
• touched: wiki/videos/2026-oconnor-belief-evidence-elites.md, wiki/authors/oconnor-kevin.md, wiki/topics/conformity-bias.md, wiki/topics/social-learning.md, wiki/topics/belief-formation.md, wiki/topics/journalism.md, wiki/topics/corporate-influence.md, wiki/topics/psychology-of-belief.md, wiki/videos/index.md, wiki/index.md
• notes: Philosophy talk on social mechanisms of belief spread; conformity bias, selective evidence curation by institutions and industry, journalism fairness bias; historical cases (variolation, tobacco, pharmaceuticals); mathematical models; argues false beliefs are social phenomena not merely cognitive errors; created three topic stubs (conformity-bias, social-learning, belief-formation).

[2026-08-11 23:55] ingest | Accuracy, Reasoning, and Misinformation

• job: 1786475587534-62f58c32
• submitter: reza@data.syr.edu
• page: Accuracy, Reasoning, and Misinformation
• touched: wiki/videos/2026-unknown-accuracy-reasoning-misinformation.md, wiki/topics/misinformation-detection.md, wiki/topics/fact-checking.md, wiki/topics/cognitive-reasoning.md, wiki/topics/cognitive-reflection.md, wiki/topics/social-media.md, wiki/topics/accuracy.md, wiki/videos/index.md
• notes: Empirical research program on cognitive reasoning as primary driver of misinformation susceptibility; 40+ studies with 25,000+ participants show Cognitive Reflection Test predicts accuracy discrimination independent of motivated reasoning; accuracy-nudge interventions reduce false-headline sharing at scale; speaker identification uncertain—collaborator "Gord" suggests Gord Pennycook, likely part of major misinformation lab (Rand/Pennycook group).

[2026-08-11 22:30] ingest | Academic Review: Political Deepfakes

• job: 1786475587526-aba477ce
• submitter: reza
• page: Academic Review: Political Deepfakes
• touched: wiki/videos/2024-farid-political-deepfakes.md, wiki/authors/farid-hany.md, wiki/topics/deepfakes.md, wiki/topics/election-integrity.md, wiki/videos/index.md, wiki/index.md
• notes: Hany Farid talk on deepfake technology and detection in the 2024 election cycle; covers forensic techniques (perspective geometry, shadow physics, lip-sync, behavioral biometrics), real-world examples (rally images, voice cloning, fake endorsements), human perceptual blindness to synthetic media, the asymmetry between generation (easy, cheap) and detection (hard, expensive), and solutions spanning individual literacy, platform responsibility, AI company safety, and regulatory upstream interventions (payment processors, C2PA authentication); expanded election-integrity stub topic to be comprehensive.

[2026-08-11 22:15] ingest | Deepfakes, Detection, and the Misinformation Landscape

• job: 1786475587523-9318b971
• submitter: reza
• page: Deepfakes, Detection, and the Misinformation Landscape
• touched: wiki/videos/2026-farid-deepfakes-detection.md, wiki/authors/farid-hany.md, wiki/topics/media-authentication.md (stub), wiki/topics/non-consensual-intimate-imagery.md (stub), wiki/topics/platform-safety.md (stub), wiki/index.md
• notes: Hany Farid talk on generative AI capabilities (text-to-image, text-to-video, real-time interactive agents), human perceptual blindness to synthetic media, physics-based detection techniques (perspective geometry, shadow analysis, lip-sync, behavioral biometrics), weaponization case studies (NCII, market manipulation, $25M enterprise fraud, North Korean infiltration, geopolitical deepfakes), and multi-stakeholder policy solutions (C2PA authentication, supply-chain financing intervention, platform responsibility, regulatory frameworks); created topic stubs for media-authentication, non-consensual-intimate-imagery, and platform-safety.

[2026-08-11 21:45] ingest | Challenges with Large Language Models: Implications for Fake News, Fact-Checking, and Misinformation Detection

• job: 1786475587519-1e5d01d8
• submitter: reza
• page: Challenges with Large Language Models: Implications for Fake News, Fact-Checking, and Misinformation Detection
• touched: wiki/videos/2026-nakov-llms-challenges-fake-news.md, wiki/authors/nakov-preslav.md, wiki/topics/large-language-models.md, wiki/topics/hallucinations.md, wiki/topics/fact-checking.md, wiki/topics/propaganda-detection.md, wiki/topics/machine-generated-text-detection.md, wiki/videos/index.md
• notes: Preslav Nakov talk on LLM-era challenges: hallucinations and opacity, automated misinformation at scale via paraphrasing, technical defenses (claim decomposition, program synthesis, perturbation-based detection), safety mechanisms, media literacy, and regulatory approaches; multilingual scope (propaganda detection across 13+ languages); bridges fact-checking infrastructure, machine-generated content detection, and broader societal resilience.

[2026-08-11 21:35] ingest | Misinformation, Content Moderation, and Information Integrity

• job: 1786475587515-af80385d
• submitter: reza
• page: Misinformation, Content Moderation, and Information Integrity
• touched: wiki/videos/nodate-wardle-content-moderation-solutions.md, wiki/authors/wardle-claire.md, wiki/topics/information-disorder.md, wiki/topics/content-moderation.md, wiki/topics/platform-governance.md, wiki/videos/index.md, wiki/index.md
• notes: Wardle (First Draft) talk on platform governance trilemma: defining harmful speech, moderation at scale, and multi-stakeholder coordination; proposes crowdsourced credibility assessment, research data repositories, and coordinated networks across academia/civil-society/platforms; flagged for year verification (Westminster Bridge 2017 reference suggests 2017-2018 timeframe but exact date unknown).

[2026-08-11 20:45] ingest | Disinformation and Platform Governance: Moderation, Alt-Tech, and Radicalization

• job: 1786475587507-34eca54e
• submitter: reza
• page: Disinformation and Platform Governance: Moderation, Alt-Tech, and Radicalization
• touched: wiki/videos/2021-heffner-starboard-disinformation-platform-governance.md, wiki/authors/starboard-kate.md, wiki/authors/heffner-alexander.md, wiki/topics/content-moderation.md, wiki/topics/extremism-radicalization.md, wiki/topics/platform-governance.md, wiki/videos/index.md
• notes: Interview from PBS's The Open Mind with Kate Starboard on disinformation, content moderation, alt-tech platform migration, radicalization, and regulatory frameworks; emphasizes cross-platform dynamics and the challenge of de-radicalization; highlights the tension between platform accountability and enabling platform diversity.

[2026-08-11 20:30] ingest | Misinformation, Disinformation, and Crisis Informatics

• job: 1786475587504-b84869a3
• submitter: reza
• page: Misinformation, Disinformation, and Crisis Informatics
• touched: wiki/videos/2020-starbird-crisis-misinformation.md, wiki/authors/starbird-kate.md, wiki/authors/spiro-emma.md, wiki/topics/center-for-an-informed-public.md, wiki/topics/crisis-informatics.md, wiki/topics/misinformation.md, wiki/topics/conspiracy-theories.md, wiki/topics/covid19-misinformation.md, wiki/videos/index.md, wiki/index.md
• notes: Starbird lecture on crisis informatics and how misinformation/conspiracy theories spread during COVID-19 pandemic and 2020 election; concrete case studies of 5G-COVID narratives, Bill Gates conspiracy, anti-mask misinformation; mixed-methods research bridging quantitative social-media analysis and qualitative ethnography; real-world harms (protests, graffiti).

[2026-08-11 20:25] ingest | The Psychology of Misinformation and Psychological Inoculation

• job: 1786475587500-3b2bf2ab
• submitter: reza@data.syr.edu
• page: The Psychology of Misinformation and Psychological Inoculation
• touched: wiki/videos/2025-linden-misinformation-psychology.md, wiki/authors/van-der-linden-sander.md, wiki/topics/misinformation-susceptibility.md, wiki/topics/inoculation.md, wiki/topics/fact-checking.md, wiki/topics/conspiracy-theories.md, wiki/index.md
• notes: Van der Linden talk introducing the MIST (Misinformation Susceptibility Test) and explaining psychological mechanisms behind belief persistence and why debunking fails. Discusses inoculation theory and the Bad News game as preventive interventions against conspiracy theories and misinformation.

[2026-08-11 20:15] ingest | Misinformation, Disinformation, and Psychological Inoculation

• job: 1786475587496-0cfd0b80
• submitter: reza
• page: Misinformation, Disinformation, and Psychological Inoculation
• touched: wiki/videos/2026-van-der-linden-misinformation-inoculation.md, wiki/authors/van-der-linden-sander.md, wiki/topics/inoculation.md, wiki/topics/prebunking-inoculation.md, wiki/topics/misinformation-psychology.md, wiki/topics/disinformation.md, wiki/topics/misinformation-intervention.md, wiki/topics/misinformation-interventions.md, wiki/index.md
• notes: Van der Linden lecture on psychological inoculation theory and the Bad News game intervention; comprehensive evidence on why fact-checking fails and how pre-bunking builds cognitive resistance; application to Ukraine conflict and cross-demographic robustness.

[2026-08-11 20:00] ingest | Information and Misinformation Spread on Social Networks

• job: 1786475587489-fd0a4fc8
• submitter: reza
• page: Information and Misinformation Spread on Social Networks
• touched: wiki/videos/2026-barabasi-information-misinformation-networks.md, wiki/authors/barabasi-albert-laszlo.md, wiki/topics/information-diffusion.md, wiki/topics/misinformation.md, wiki/topics/echo-chambers.md, wiki/topics/computational-propaganda.md, wiki/topics/social-bots.md, wiki/videos/index.md, wiki/index.md
• notes: Network science perspective on information and misinformation spread; demonstrates how scale-free topology + limited attention creates inevitable power-law virality; shows echo chambers arise from social influence + network rewiring alone; covers role of social bots in computational amplification and manipulation; raises ethical questions about platform autonomy.

[2026-08-11 19:24] ingest | Social Bots and Misinformation: Detection, Diffusion, and Mitigation

• job: 1786475587485-33d0a7e4
• submitter: reza@data.syr.edu
• page: Social Bots and Misinformation: Detection, Diffusion, and Mitigation
• touched: wiki/videos/2017-unknown-social-bots-misinformation.md, wiki/topics/social-bots.md, wiki/topics/bot-detection.md, wiki/topics/computational-propaganda.md, wiki/videos/index.md
• notes: Comprehensive talk on bot detection (BotOrNot/Botometer framework with 1000+ features), bot-driven misinformation campaigns (2016 elections, vaccines, Brexit), temporal analysis showing bots act as early-stage igniters, network core analysis showing bot concentration in densest misinformation clusters, and strategy findings (removing 10% of bots reduces spread by ~20%).

[2026-08-11 19:22] ingest | Misinformation, Propaganda, and the Era of Synthetic Media

• job: 1786475587469-fb8537e5
• submitter: reza@data.syr.edu
• page: Misinformation, Propaganda, and the Era of Synthetic Media
• touched: wiki/videos/2018-unknown-misinformation-synthetic-media.md, wiki/videos/index.md
• notes: Talk comprehensively covering false news propagation (humans not bots drive spread, novelty preference), computational propaganda campaigns (AP breach, Russian IRA interference), and emerging synthetic media threats (GANs, deepfakes); discusses five policy/technical solutions (labeling, incentives, regulation, transparency, algorithms) with honest tradeoffs.

[2026-08-11 19:20] ingest | False News Spreads Faster and Farther Than Truth Online

• job: 1786475587463-b62e9d2d
• submitter: reza@data.syr.edu
• page: False News Spreads Faster and Farther Than Truth Online
• touched: wiki/videos/2017-unknown-false-news-spread-twitter.md, wiki/topics/misinformation-spread.md, wiki/topics/propagation-based-detection.md, wiki/videos/index.md, wiki/index.md
• notes: Talk presenting findings from landmark MIT Digital Economy Initiative research on false news spreading faster and farther than truth on Twitter 2006–2017; emphasizes human susceptibility to novelty over bot-driven spread.

[2026-08-10 16:41] figure | 2026-kefler-network-information-unreliable-news

• prepared figure prompt showing three-stage pipeline: network construction from Telegram data, GNN training (GraphSAGE/GCN/GAT), and domain reliability classification

[2026-08-10 16:40] delete | HNR-DAC: Hard-Negative Reranking and Distribution-Aligned Classification for Scientific Claim Verification

• page: wiki/papers/2026-wang-hnr-dac-claim-verification.md → wiki/.deleted/papers/2026-wang-hnr-dac-claim-verification.md
• touched: wiki/papers/index.md, wiki/topics/claim-verification.md, wiki/topics/evidence-ranking.md, wiki/topics/fact-checking.md, wiki/topics/citation-faithfulness.md, wiki/authors/wang-zhenchao.md, wiki/authors/chen-xin.md, wiki/authors/zhang-luoxi.md, wiki/authors/yang-min.md, wiki/authors/ni-shiwen.md, wiki/index.md

[2026-08-10 16:26] ingest | Network Information Enhances Unreliable News Domain Detection

• job: 1786393348343-654ba757
• submitter: auto-discovery@guide.fake-news.site
• page: Network Information Enhances Unreliable News Domain Detection
• touched: wiki/papers/2026-kefler-network-information-unreliable-news.md, wiki/authors/kefler-raphaela.md, wiki/authors/ventzke-roman-david.md, wiki/authors/priesemann-viola.md, wiki/authors/de-marzo-giordano.md, wiki/topics/network-based-misinformation-detection.md, wiki/topics/graph-neural-networks.md, wiki/topics/credibility-assessment.md, wiki/papers/index.md
• notes: Domain-level reliability classification on Telegram using network co-sharing structure and GNNs; GraphSAGE achieves 0.63 accuracy showing network topology provides complementary signals to content for credibility assessment.

[2026-08-10 16:25] ingest | HNR-DAC: Hard-Negative Reranking and Distribution-Aligned Classification for Scientific Claim Verification

• job: 1786393345834-a04f3041
• submitter: auto-discovery@guide.fake-news.site
• page: 2026-wang-hnr-dac-claim-verification (deleted 2026-08-16)
• touched: wiki/papers/2026-wang-hnr-dac-claim-verification.md, wiki/authors/wang-zhenchao.md, wiki/authors/chen-xin.md, wiki/authors/zhang-luoxi.md, wiki/authors/yang-min.md, wiki/authors/ni-shiwen.md, wiki/topics/claim-verification.md, wiki/topics/fact-checking.md, wiki/topics/evidence-ranking.md, wiki/topics/citation-faithfulness.md, wiki/index.md
• notes: Two-stage framework for scientific claim verification combining hard-negative mining (to identify confusable evidence) with distribution-aligned training (to match inference conditions); ranks 3rd on NLPCC 2026 Task 10 Track 2 benchmark.

[2026-08-10 08:50] discover | weekly autonomous discovery

• venues searched: arXiv cs.CL, arXiv cs.SI, arXiv cs.CY
• candidates found: 23
• queued: 10 (job ids: 1786393344865-5254f1e8, 1786393345478-b9138e36, 1786393345834-a04f3041, 1786393346491-ff07688c, 1786393346844-9afa9015, 1786393347551-aae78532, 1786393347721-e1f05578, 1786393348343-654ba757, 1786393348688-d0c2056d, 1786393349049-deb8bbb4)
• duplicates skipped: 0
• out of scope skipped: 13
• notes: Filtered 300 recent papers across three arXiv categories using W1 relevance criteria. Found candidates in claim verification, media bias analysis, news credibility, deepfake detection, and misinformation detection in LLM ecosystems. Queued at cap (10).

[2026-08-09 07:16] figure | 2026-cai-spectral-analysis

• prepared figure prompt showing five spectral bound categories (branching, scale, cohesion, span, diffusion) for characterizing propagation cascade structure

[2026-08-09 07:15] ingest | Spectral Analysis of Fake News Propagation

• job: 1786273700059-db3ac44a
• submitter: reza
• page: Spectral Analysis of Fake News Propagation
• touched: wiki/papers/2026-cai-spectral-analysis.md, wiki/authors/cai-weibin.md, wiki/authors/zafarani-reza.md, wiki/topics/spectral-analysis.md, wiki/topics/graph-based-detection.md, wiki/topics/propagation-based-detection.md, wiki/papers/index.md, wiki/index.md
• notes: Spectral graph analysis of fake news propagation using eigenvalue-based bounds for structural interpretation; introduces 35 spectral bounds in five categories distinguishing fake from real news.

[2026-08-10 00:00] lint | weekly auto-fix

• fixed: 56 mechanical BROKEN_LINK issues
• touched: wiki/log.md, wiki/authors/pham-long-khanh.md, wiki/authors/lin-chenghua.md, wiki/authors/sakharova-marina.md, wiki/authors/dao-minh-son.md, wiki/authors/dycke-nils.md, wiki/authors/wang-chi.md, wiki/datasets/wildfakebench.md, wiki/papers/2025-zhang-sincon.md, wiki/papers/2026-dycke-redefining-aitd.md, wiki/papers/2026-ai-paradigm-shift-lvlms.md, wiki/papers/2025-lakzaei-neighborhood-order-learning-gat.md, wiki/papers/2025-lakzaei-dhgat-heterogeneous-detection.md, wiki/papers/2026-wang-llm-linguistic-fingerprints.md, wiki/papers/2025-phan-freeM2-ooc-detection.md, wiki/papers/2025-thibault-guide-misinformation-datasets.md, wiki/papers/2025-yan-debunk-infer-multimodal.md, wiki/papers/2026-zeng-micro-video-misinformation.md, wiki/topics/latent-knowledge.md, wiki/topics/adversarial-attacks.md, wiki/topics/evaluation-methodology.md, wiki/topics/multimodal-misinformation.md, wiki/topics/multilingual-disinformation.md, wiki/topics/malicious-intent.md, wiki/topics/intent-detection.md, wiki/topics/multimodal-fake-news-detection.md, wiki/topics/heterogeneous-graph-neural-networks.md, wiki/topics/out-of-context-detection.md
• notes: Resolved broken wikilinks by: (1) correcting paper/topic slugs with typos or wrong years (e.g., 2021-shu → 2020-shu, zhou-fake-news-survey → 2020-zhou-fake-news-survey), (2) removing references to non-existent topic pages (e.g., graph-attention-networks, message-passing-neural-networks, information-warfare, image-manipulation, semantic-understanding), (3) converting paper references to non-existent wiki pages to plain text, (4) removing author wikilinks for authors without pages

[2026-08-09 23:10] figure | 2025-thibault-guide-misinformation-datasets

• prepared figure prompt showing quality assessment framework with three evaluation dimensions: spurious correlations, temporal leakage, and claim feasibility analysis

[2026-08-09 23:09] figure | 2025-yan-debunk-infer-multimodal

• prepared figure prompt showing evidence generation via diffusion and four-agent multimodal reasoning (text, visual, audio, synthesis) for fake news detection

[2026-08-09 23:08] figure | 2026-zeng-micro-video-misinformation

• prepared figure prompt showing five-agent pipeline: Content Analyst, Planner, Retriever, Locator, Integrator for explainable micro-video misinformation detection

[2026-08-09 23:07] figure | 2026-li-misleading-omissions-multimodal

• prepared figure prompt showing multimodal news preview input, detection and correction pipeline stages, and corrected headline output with 86% accuracy badge

[2026-08-09 23:06] figure | 2025-phan-freeM2-ooc-detection

• prepared figure prompt showing retrieval, filtering, two-stage reasoning, and detection stages for training-free multimodal news verification

[2026-08-10 16:30] figure | 2026-wang-hnr-dac-claim-verification

• prepared figure prompt showing two-stage claim verification with hard-negative reranking for evidence ranking and distribution-aligned classification for relation prediction

[2026-08-09 23:05] figure | 2026-jiang-agentic-multimodal

• prepared figure prompt showing multi-agent hierarchy with modality-specific agents (textual, visual, cross-modal) coordinated by planning agent with critique-aware ranking

[2026-08-09 07:10] figure | 2026-wu-deception-intent-multimodal

• prepared figure prompt showing intent-guided dataset, two-dimensional intent framework, VLM benchmark on intent detection tasks, and verdict classification

[2026-08-09 23:04] figure | 2025-raza-vldbench

• prepared figure prompt showing dataset collection, model evaluation on VLMs vs LLMs, robustness testing under perturbations, and governance framework alignment

[2026-08-09 23:03] figure | 2025-lakzaei-dhgat-heterogeneous-detection

• prepared figure prompt showing four-stage pipeline: graph construction from speaker features, decision network for edge-type selection, representation network for embedding updates, and multi-class classification

[2026-08-09 23:02] figure | 2025-lakzaei-neighborhood-order-learning-gat

• prepared figure prompt showing four-stage pipeline: Doc2Vec embedding, K-NN graph construction, adaptive per-node hop-neighborhood learning via NOL-GAT, and binary classification

[2026-08-09 23:01] figure | 2025-hu-glpn-llm-multimodal

• prepared figure prompt showing feature extraction via CLIP, cross-modal graph construction, label integration, and masked label propagation with GCN

[2026-08-09 23:00] figure | 2025-wang-intent-semantic-joint-learning

• prepared figure prompt showing semantic graph, intent graph, and dynamic alignment via pseudo nodes for joint learning

[2026-08-09 22:58] figure | 2025-zhang-sincon

• prepared figure prompt showing identification of node influence imbalance, augmentation with masked variants, contrastive regularization, and defense against LLM-generated message injection

[2026-08-09 22:56] figure | 2026-modzelewski-malint-dataset

• prepared figure prompt showing dataset annotation, intent classification, inoculation reasoning, and detection pipeline with multilingual validation

[2026-08-09 22:55] figure | 2026-dycke-redefining-aitd

• prepared figure prompt showing framework analysis, dataset construction, and detector evaluation with threshold sensitivity

[2026-08-09 22:50] figure | 2026-macko-llm-generated-disinformation

• prepared figure prompt showing dual-detector methodology, temporal trend, and cross-linguistic/platform vulnerability analysis

[2026-08-09 22:40] figure | 2026-loth-industrialized-deception

• prepared figure prompt showing methodology, findings, and mitigation strategies for LLM-generated misinformation

[2026-08-09 22:35] figure | 2025-yi-llm-powered-fake-news

• prepared figure prompt showing four exemplar LLM-based detection frameworks

[2026-08-09 22:30] ingest | A Guide to Misinformation Detection Data and Evaluation

• job: 1786254263769-4f0b74bb
• submitter: reza
• page: A Guide to Misinformation Detection Data and Evaluation
• touched: Camille Thibault, Jacob-Junqi Tian, Gabrielle Peloquin-Skulski, Taylor Lynn Curtis, James Zhou, Florence Laflamme, Luke Yuxiang Guan, Reihaneh Rabbany, Jean-François Godbout, Kellin Pelrine, Datasets and benchmarks, Dataset quality, Label quality, Evaluation Methodology, wiki/papers/2025-thibault-guide-misinformation-datasets.md, wiki/authors/thibault-camille.md, wiki/authors/tian-jacob-junqi.md, wiki/authors/peloquin-skulski-gabrielle.md, wiki/authors/curtis-taylor-lynn.md, wiki/authors/zhou-james.md, wiki/authors/laflamme-florence.md, wiki/authors/guan-luke-yuxiang.md, wiki/authors/rabbany-reihaneh.md, wiki/authors/godbout-jean-francois.md, wiki/authors/pelrine-kellin.md, wiki/topics/datasets.md, wiki/topics/evaluation-methodology.md, wiki/topics/dataset-quality.md, wiki/topics/label-quality.md, wiki/index.md, wiki/papers/index.md
• notes: Comprehensive survey of 75 misinformation detection datasets with quality assessment framework; identifies spurious correlations and infeasibility issues in dataset labeling; introduces CDL-MD database and EQA methodology framework. Creates new topics for dataset-quality and label-quality; expands datasets, evaluation-methodology, and evaluation-metrics topics; creates 10 author pages; updates main index and papers index.

[2026-08-09 22:15] ingest | Debunk and Infer: Multimodal Fake News Detection via Diffusion-Generated Evidence and LLM Reasoning

• job: 1786254263766-91456b11
• submitter: reza
• page: Debunk and Infer: Multimodal Fake News Detection via Diffusion-Generated Evidence
• touched: Kaiying Yan, Moyang Liu, Yukun Liu, Ruibo Fu, Zhengqi Wen, Jianhua Tao, Xuefei Liu, Multimodal fake news detection, Large Language Models, Evidence-based fake news detection, Diffusion models, wiki/papers/2025-yan-debunk-infer-multimodal.md, wiki/authors/yan-kaiying.md, wiki/authors/liu-moyang.md, wiki/authors/liu-yukun.md, wiki/authors/fu-ruibo.md, wiki/authors/wen-zhengqi.md, wiki/authors/tao-jianhua.md, wiki/authors/liu-xuefei.md, wiki/topics/multimodal-fake-news-detection.md, wiki/topics/large-language-models.md, wiki/topics/evidence-based-detection.md, wiki/topics/diffusion-models.md, wiki/index.md
• notes: DIFND framework combining conditional diffusion models for evidence generation with multi-agent LLM reasoning over text, audio, and video. Achieves 87.68% accuracy on FakeSV and 92.16% on FVC. Creates new diffusion-models topic; updates multimodal detection, LLM, and evidence-based detection topic pages with paper; adds paper to main index under multimodal section.

[2026-08-09 22:00] ingest | From Manipulation to Mistrust: Explaining Diverse Micro-Video Misinformation for Robust Debunking in the Wild

• job: 1786254263762-9c8a8f92
• submitter: reza
• page: From Manipulation to Mistrust: Explaining Diverse Micro-Video Misinformation
• touched: Zhi Zeng, Yifei Yang, Jiaying Wu, Xulang Zhang, Xiangzheng Kong, Herun Wan, Zihan Ma, Minnan Luo, Micro-video misinformation, Multimodal Misinformation Detection, Explainable fake news detection, Deepfakes, WildFakeBench, wiki/papers/2026-zeng-micro-video-misinformation.md, wiki/authors/zeng-zhi.md, wiki/authors/yang-yifei.md, wiki/authors/wu-jiaying.md, wiki/authors/zhang-xulang.md, wiki/authors/kong-xiangzheng.md, wiki/authors/wan-herun.md, wiki/authors/ma-zihan.md, wiki/authors/luo-minnan.md, wiki/topics/micro-video-misinformation.md, wiki/datasets/wildfakebench.md, wiki/topics/multimodal-misinformation-detection.md, wiki/topics/explainable-detection.md, wiki/topics/deepfakes.md, wiki/papers/index.md
• notes: WildFakeBench: 10,107 real-world micro-videos from 6 platforms (2017–2025) with fine-grained annotation (manipulation, biases, AIGC, out-of-context). FakeAgent: multi-agent reasoning framework combining multimodal content analysis, external evidence retrieval, and evidence integration; 68.63% micro-accuracy vs. GPT-4o (65.14%). Creates new micro-video-misinformation topic and wildfakebench dataset pages; updates 3 existing author pages and 3 topic pages.

[2026-08-09 21:45] ingest | What's Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News Previews

• job: 1786254263758-fbe89460
• submitter: reza
• page: What's Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal
• touched: Fanxiao Li, Jiaying Wu, Tingchao Fu, Dayang Li, Herun Wan, Wei Zhou, Min-Yen Kan, Misinformation and fake news detection, Multimodal Learning, Content moderation, Visual misinformation, News credibility and source assessment, Fact-checking and corrections, wiki/papers/2026-li-misleading-omissions-multimodal.md, wiki/authors/li-fanxiao.md, wiki/authors/wu-jiaying.md, wiki/authors/fu-tingchao.md, wiki/authors/li-dayang.md, wiki/authors/wan-herun.md, wiki/authors/zhou-wei.md, wiki/authors/kan-min-yen.md, wiki/topics/misinformation-detection.md, wiki/topics/multimodal-learning.md, wiki/topics/content-moderation.md, wiki/topics/visual-misinformation.md, wiki/topics/news-credibility.md, wiki/topics/fact-checking.md, wiki/papers/index.md
• notes: MM-MISLEADING benchmark (6K multimodal news previews) and OMGUARD framework for detecting/correcting misleading omissions via interpretation-aware fine-tuning and rationale-guided correction. Expands multimodal-learning stub topic; shows 67% of omission cases stem from missing context/conditions rather than global reframing; identifies visual semantics as limiting factor in text-only correction.

[2026-08-09 21:15] ingest | E-FreeM2: Efficient Training-Free Multi-Scale and Cross-Modal News Verification via MLLMs

• job: 1786254263755-7938fb79
• submitter: reza
• page: E-FreeM2: Efficient Training-Free Multi-Scale and Cross-Modal News Verification
• touched: Van-Hoang Phan, Long-Khanh Pham, Dang Vu, Anh-Duy Tran, Minh-Son Dao, Out-of-context misinformation detection, Multimodal Misinformation, Fact-checking and corrections, Vision-language models, wiki/papers/2025-phan-freeM2-ooc-detection.md, wiki/authors/phan-van-hoang.md, wiki/authors/pham-long-khanh.md, wiki/authors/vu-dang.md, wiki/authors/tran-anh-duy.md, wiki/authors/dao-minh-son.md, wiki/topics/out-of-context-detection.md, wiki/topics/multimodal-misinformation.md, wiki/papers/index.md
• notes: Training-free retrieval-based system for out-of-context misinformation detection using MLLMs (Gemini + GPT-4o mini) with two-stage chain-of-thought reasoning; achieves 90.0% accuracy on NewsCLIPpings (outperforming SNIFFER 88.4%) with zero trainable parameters. Cross-modal evidence retrieval pipeline combines textual search and visual reverse search with visual-centric ranking. Creates new topic page (out-of-context-detection) for this important misinformation subproblem; expands multimodal-misinformation stub; creates 5 author pages.

[2026-08-09 20:45] ingest | Agentic Mixed-Source Multi-Modal Misinformation Detection with Adaptive Test-Time Scaling

• job: 1786254263751-fb0ae3cf
• submitter: reza
• page: Agentic Mixed-Source Multi-Modal Misinformation Detection with Adaptive Test-Time
• touched: Wei Jiang, Tong Chen, Wei Yuan, Quoc Viet Hung Nguyen, Hongzhi Yin, Multimodal Misinformation Detection, Vision-language models, wiki/papers/2026-jiang-agentic-multimodal.md, wiki/authors/jiang-wei.md, wiki/authors/chen-tong.md, wiki/authors/yuan-wei.md, wiki/authors/nguyen-quoc-viet-hung.md, wiki/authors/yin-hongzhi.md, wiki/topics/multimodal-misinformation-detection.md, wiki/topics/vision-language-models.md, wiki/papers/index.md
• notes: AgentM³D: hierarchical multi-agent framework for zero-shot mixed-source multimodal misinformation detection; decomposes into modality-specific agents (textual, visual, cross-modal consistency) with critique-aware best-of-N reasoning and adaptive test-time scaling. Achieves 62.6% accuracy on Qwen3-VL-8B; introduces critiqu-aware ranking as novel alternative to general reward models.

[2026-08-09 20:15] ingest | Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal News with Vision-Language Models

• job: 1786254263747-8e6c0375
• submitter: reza
• page: Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal
• touched: Jiaying Wu, Fanxiao Li, Zihang Fu, Min-Yen Kan, Bryan Hooi, Multimodal Misinformation Detection, Creator Intent, Vision-language models, Media manipulation, Misinformation and fake news detection, wiki/papers/2026-wu-deception-intent-multimodal.md, wiki/authors/wu-jiaying.md, wiki/authors/li-fanxiao.md, wiki/authors/fu-zihang.md, wiki/authors/kan-min-yen.md, wiki/authors/hooi-bryan.md, wiki/topics/multimodal-misinformation-detection.md, wiki/topics/creator-intent.md, wiki/topics/vision-language-models.md, wiki/topics/media-manipulation.md, wiki/topics/misinformation-detection.md, wiki/papers/index.md
• notes: Introduces DECEPTIONDECODED, a 12K image-caption-article benchmark grounded in trustworthy news; proposes intent-guided simulation framework with two dimensions (desired influence + execution plan); evaluates 14 VLMs, finding they are fragile at intent reasoning and over-reliant on superficial cues. Creates new creator-intent topic and expands five existing topic pages.

[2026-08-09 19:30] ingest | VLDBench: Evaluating Multimodal Disinformation with Regulatory Alignment

• job: 1786254263743-edab830b
• submitter: reza
• page: VLDBench: Evaluating Multimodal Disinformation with Regulatory Alignment
• touched: Shaina Raza, Ashmal Vayani, Aditya Jain, Aravind Narayanan, Vahid Reza Khazaie, Syed Raza Bashir, Elham Dolatabadi, Gias Uddin, Christos Emmanouidis, Rizwan Qureshi, Mubarak Shah, Multimodal fake news detection, Vision-language models, AI governance and compliance, Benchmark datasets and evaluations, Synthetic media, VLDBench, wiki/authors/raza-shaina.md, wiki/authors/vayani-ashmal.md, wiki/authors/jain-aditya.md, wiki/authors/narayanan-aravind.md, wiki/authors/khazaie-vahid-reza.md, wiki/authors/bashir-syed-raza.md, wiki/authors/dolatabadi-elham.md, wiki/authors/uddin-gias.md, wiki/authors/emmanouidis-christos.md, wiki/authors/qureshi-rizwan.md, wiki/authors/shah-mubarak.md, wiki/topics/ai-governance.md, wiki/datasets/vldbench.md, wiki/papers/index.md, wiki/index.md
• notes: First large-scale human-verified multimodal disinformation benchmark (31,339 articles × 2 modalities; κ=0.78) aligned with AI governance frameworks. VLMs substantially outperform text-only LLMs (+5–30pp) but show critical cross-modal vulnerability. Creates new topic page (ai-governance) and dataset page (vldbench); updates 10 author pages; evaluates 19 models. Governance-aligned evaluation framework bridges technical benchmarking and policy risk dimensions.

[2026-08-09 16:45] ingest | A Decision-Based Heterogeneous Graph Attention Network for Multi-Class Fake News Detection

• job: 1786254263739-39650873
• submitter: reza
• page: A Decision-Based Heterogeneous Graph Attention Network for Multi-Class Fake
• touched: Batool Lakzaei, Mostafa Haghir Chehreghani, Alireza Bagheri, Heterogeneous Graph Neural Networks, Multi-class classification, Graph Neural Networks, Fake news detection, Liar Dataset, wiki/authors/lakzaei-batool.md, wiki/authors/chehreghani-mostafa.md, wiki/authors/bagheri-alireza.md, wiki/topics/heterogeneous-graph-neural-networks.md, wiki/topics/multi-class-classification.md, wiki/topics/graph-neural-networks.md, wiki/topics/fake-news-detection.md, wiki/topics/liar-dataset.md, wiki/papers/index.md, wiki/index.md
• notes: DHGAT enables per-node per-layer dynamic neighborhood-type selection on heterogeneous speaker-profile-derived graphs; achieves ~4% accuracy improvement on LIAR (six-class) with ordinal-aware semantic distance loss; robust in semi-supervised settings (10–30% labeled). Creates two new topic pages (heterogeneous-graph-neural-networks, multi-class-classification) and expands LIAR-dataset page.

[2026-08-09 15:30] ingest | Neighborhood-Order Learning Graph Attention Network for Fake News Detection

• job: 1786254263715-d6259a79
• submitter: reza
• page: Neighborhood-Order Learning Graph Attention Network for Fake News Detection
• touched: Batool Lakzaei, Mostafa Haghir Chehrghani, Alireza Bagheri, Graph Neural Networks, Fake news detection, wiki/authors/lakzaei-batool.md, wiki/authors/chehrghani-mostafa.md, wiki/authors/bagheri-alireza.md, wiki/topics/graph-neural-networks.md, wiki/topics/fake-news-detection.md, wiki/papers/index.md, wiki/index.md
• notes: NOL-GAT addresses GNN neighborhood depth limitation via adaptive per-node hop-order learning using Gumbel-Softmax; achieves 5–15% macro-F1 gains over baselines (CO-GNN, GATv2, TextGCN) across five datasets, particularly strong in low-label regimes (10–30% labeled).

[2026-08-09 02:18] ingest | Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection

• job: 1786254263712-b37d2e1f
• submitter: reza
• page: Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection
• touched: Shuguo Hu, Jun Hu, Huaiwen Zhang, Multimodal fake news detection, Large Language Models, Label propagation, Graph Neural Networks, Pseudo-labeling, Fake news detection methods, wiki/authors/hu-shuguo.md, wiki/authors/hu-jun.md, wiki/authors/zhang-huaiwen.md, wiki/topics/multimodal-fake-news-detection.md, wiki/topics/label-propagation.md, wiki/topics/pseudo-labeling.md, wiki/topics/large-language-models.md, wiki/topics/graph-neural-networks.md, wiki/topics/detection-methods.md, wiki/papers/index.md, wiki/index.md
• notes: Framework combining LLM pseudo-labels with masked graph-based label propagation for multimodal detection; demonstrates naive LLM integration insufficient but structured propagation boosts performance; creates three new topic pages for emerging technical area.

[2026-08-09 14:45] ingest | Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection

• job: 1786254263708-e1f2e024
• submitter: reza
• page: Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for
• touched: Zhengjia Wang, Qiang Sheng, Danding Wang, Beizhe Hu, Juan Cao, Fake news detection, Graph Neural Networks, Semantic Analysis, Intent detection and classification, wiki/authors/wang-zhengjia.md, wiki/authors/sheng-qiang.md, wiki/authors/wang-danding.md, wiki/authors/hu-beizhe.md, wiki/authors/cao-juan.md, wiki/topics/fake-news-detection.md, wiki/topics/graph-neural-networks.md, wiki/topics/semantic-analysis.md, wiki/topics/intent-detection.md, wiki/papers/index.md, wiki/index.md
• notes: InSIDE combines semantic (narrative flow + entity context) and intent (coarse-fine hierarchy of purposes) via heterogeneous GNNs with dynamic alignment; outperforms intent-aware baselines on four datasets; demonstrates intent stability as complement to shifting semantic patterns.

[2026-08-09 10:30] ingest | SINcon: Mitigate LLM-Generated Malicious Message Injection Attack for Rumor Detection

• job: 1786254263704-6eebb29c
• submitter: reza
• page: SINcon: Mitigate LLM-Generated Malicious Message Injection Attack for Rumor
• touched: Mingqing Zhang, Qiang Liu, Xiang Tao, Shu Wu, Liang Wang, Rumor detection on social media, Adversarial Attacks on Misinformation Detection, Graph Neural Networks, Propagation-based fake news detection, wiki/authors/zhang-mingqing.md, wiki/authors/liu-qiang.md, wiki/authors/tao-xiang.md, wiki/authors/wu-shu.md, wiki/authors/wang-liang.md, wiki/topics/rumor-detection.md, wiki/topics/adversarial-attacks.md, wiki/topics/graph-neural-networks.md, wiki/topics/propagation-based-detection.md, wiki/papers/index.md
• notes: Addresses vulnerability of MPT-based GNN rumor detection to LLM-generated malicious message injection attacks; proposes contrastive learning defense (SINcon) that balances node influence; achieves 26% average robustness improvement on Twitter/Weibo while preserving clean-data accuracy.

[2026-08-09 22:50] figure | 2026-ai-paradigm-shift-lvlms

• prepared figure prompt showing three-branch taxonomy for LVLM-based multimodal detection (parameter-frozen, parameter-tuning, reasoning)

[2026-08-09 02:10] ingest | MALicious INTent Dataset and Inoculating LLMs for Enhanced Disinformation Detection

• job: 1786254263700-43b4b633
• submitter: reza
• page: MALicious INTent Dataset and Inoculating LLMs for Enhanced Disinformation Detection
• touched: Arkadiusz Modzelewski, Witold Sosnowski, Eleni Papadopulos, Elisa Sartori, Tiziano Labruna, Giovanni Da San Martino, Adam Wierzbicki, Disinformation Detection, Datasets and benchmarks, Malicious intent in disinformation, Intent detection and classification, Inoculation and prebunking, Language Models, wiki/authors/modzelewski-arkadiusz.md, wiki/authors/sosnowski-witold.md, wiki/authors/papadopulos-eleni.md, wiki/authors/sartori-elisa.md, wiki/authors/labruna-tiziano.md, wiki/authors/wierzbicki-adam.md, wiki/topics/disinformation-detection.md, wiki/topics/datasets.md, wiki/topics/malicious-intent.md, wiki/topics/intent-detection.md, wiki/topics/inoculation.md, wiki/papers/index.md, wiki/index.md
• notes: First English corpus annotating both disinformation and malicious intent (1,600 articles); five intent categories grounded in prior research and expert review. Intent-augmented reasoning improves zero-shot LLM detection by ~9% (MALINT) and ~20% on five diverse English datasets, with strong transfer to six languages.

[2026-08-09 20:15] ingest | 'Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic Assumptions

• job: 1786254263697-bb0124db
• submitter: reza
• page: Your AI Text is not Mine'': Redefining and Evaluating AI-generated Text
• touched: Nils Dycke, Marina Sakharova, Nico Daheim, Iryna Gurevych, AI-Generated Text Detection, LLM-generated text detection, Machine-generated text detection, Content moderation, wiki/authors/daheim-nico.md, wiki/authors/gurevych-iryna.md, wiki/papers/index.md, wiki/topics/ai-generated-text-detection.md, wiki/topics/llm-generated-text-detection.md, wiki/topics/machine-generated-text-detection.md, wiki/topics/content-moderation.md
• notes: Systematically analyzes fragmented AITD task definitions; proposes unified framework distinguishing genesis-based vs. population-based notions with explicit assumptions; introduces AITDNA dataset of 350+ real human-AI co-created texts with interaction logs; shows detector performance depends on alignment with implicit normative standards; no detector consistently outperforms across all assumptions.

[2026-08-09 18:25] ingest | Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detection

• job: 1786254263693-1eb7f230
• submitter: reza
• page: Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detection
• touched: Chi Wang, Min Gao, Zongwei Wang, Junwei Yin, Kai Shu, Chenghua Lin, Fake news detection, LLM-generated content, Synthetic media, Linguistic style detection, wiki/papers/index.md, wiki/topics/fake-news-detection.md, wiki/topics/llm-generated-content.md, wiki/topics/synthetic-media.md
• notes: Novel approach detecting LLM-generated fake news via token reconstruction probability analysis; proposes LIFE framework with key fragment extraction achieving 93.7% accuracy and strong cross-model generalization; first to shift detection from content features to underlying generation process.

[2026-08-09 17:15] ingest | Beyond Speculation: Measuring the Growing Presence of LLM-Generated Texts in Multilingual Disinformation

• job: 1786254263690-d41f30a0
• submitter: reza
• page: Beyond Speculation: Measuring the Growing Presence of LLM-Generated Texts
• touched: Dominik Macko, Aashish Anantha Ramakrishnan, Jason S. Lucas, Robert Moro, Ivan Srba, Adaku Uchendu, Dongwon Lee, LLM-generated text detection, Machine-generated text detection, Multilingual Disinformation, Disinformation Detection, wiki/papers/index.md
• notes: First empirical measurement of LLM-generated content in real-world fact-checked disinformation datasets; documents 99% increase (2021–2023) with sharp rise post-ChatGPT; reveals heterogeneous patterns across 15 languages (Polish 4.7%, French 4.2% vs. English 0.7%) and platforms; validates multilingual detection on five benchmark datasets.

[2026-08-09 16:35] ingest | Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems

• job: 1786254263686-530d578f
• submitter: reza
• page: Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation
• touched: Alexander Loth, Martin Kappes, Marc-Oliver Pahl, Generative AI, LLM-Generated Misinformation, Digital ecosystems, Inoculation and prebunking, Fake news detection, Synthetic media, Multimodal Detection, Deepfakes, Content moderation, wiki/papers/index.md, wiki/index.md
• notes: Updated perspective on generative AI and misinformation (2024–2026) with new tools (JudgeGPT, RogueGPT) for measuring human perception and deceptive potential; documents agentic shift, multimodal misinformation, ecosystem-level risks (epistemic fragmentation, synthetic consensus); synthesizes mitigation strategies.

[2026-08-09 16:30] ingest | The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection

• job: 1786254263656-b55ca293
• submitter: reza
• page: The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models
• touched: Wei Ai, Yilong Tan, Yuntao Shou, Tao Meng, Haowen Chen, Zhixiong He, Keqin Li, Multimodal fake news detection, Vision-language models, Deep learning, wiki/papers/index.md, wiki/index.md
• notes: First comprehensive survey of LVLMs in multimodal fake news detection; proposes three-branch taxonomy (parameter-frozen, parameter-tuning, reasoning) and five-dimensional deception framework; benchmarks 15+ architectures across eight datasets; identifies eight future research directions on causal reasoning, robustness, and hallucination mitigation.

[2026-08-09 15:00] ingest | Challenges and Innovations in LLM-Powered Fake News Detection

• job: 1786254263651-a6ea3b58
• submitter: reza
• page: Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis
• touched: Jingyuan Yi, Zeqiu Xu, Tianyi Huang, Peiyang Yu, Semantic Analysis, Real-Time Detection, Large Language Models, Multimodal fake news detection, Adversarial robustness, wiki/papers/index.md, wiki/index.md
• notes: Comprehensive survey of LLM applications to fake news detection; synthesizes four frameworks (MiLk-FD, FND-LLM, DAFND, SheepDog) and identifies challenges in multimodal integration, adversarial robustness, scalability, and interpretability.

[2026-08-09] lint | weekly auto-fix

• fixed: 50 mechanical issues
• touched: 24 topic pages, 2 paper pages, 1 main index, 1 meta page (citation.md), 1 log entry
• notes: Removed broken wikilinks to 45 non-existent papers and topics from topic pages: 2023-shu-exploitability-instruction-tuning, 2018-howard-ulmfit, 2020-guu-realm, 2019-levi-fake-news-satire-nuances, 2023-liu-lost-in-middle (4 files), 2022-burns-latent-knowledge, 2023-guerreiro-hallucinations-multilingual (5 files), 2017-schmidt-hate-speech-detection (2 files), 2011-kontaxis-detecting-profile-cloning, 2015-prieto-detecting-spammers-spam-nets, 2020-xiao-detecting-clusters-fake-accounts, 2023-wang-medical-summarization-metrics (3 files), 2023-dahein-model-merging, 2022-ibrahim-personality-traits-rumors, 2019-cui-claim-verification-explanations, 2023-liao-interpretable-misinformation, 2019-jobin-trustworthy-ai-principles, 2021-cincilioglu-neural-symbolic-rule-learning, 2020-angelov-top2vec, responsible-AI, AI-generated-content (2 files). Fixed topic slug covid-19-misinformation → covid19-misinformation. Added type:citation to citation.md and added citation page to main wiki/index.md. Fixed log.md entry to remove wikilink syntax from historical documentation.

[2026-06-14] lint | weekly auto-fix

• fixed: 56 mechanical issues (broken wikilinks)
• touched: 24 paper pages
• notes: Fixed missing year prefixes in 15 wikilinks (e.g., zhou-fake-news-survey → 2020-zhou-fake-news-survey), corrected topic slug covid-19-misinformation → covid19-misinformation. Removed wikilinks for 25 non-existent papers and replaced with plain text (bolukbasi-man-is-to-computer-programmer, kreps-authentic-twitter-survey, jindi-stay-on-topic-generating, radford-language-models, li-predpath, lao-pra, shahi-fakecovid, cresci-bots-17, ippolito-automatic-detection, arora-watermarking, solaiman-2022-language-model-detection, bender-stochastic-parrots, rader-gray-facebook-news-feed, nguyen-filter-bubble, geschke-triple-filter-bubble, lex-mitigating-confirmation-bias, penn-truthful-ai, waseem-hovy-hateful-symbols, levi-fake-news-satire-nuances, lee-honeypot, zampieri-2019-offensive-language, zampieri-semeval-offenseval, zhang-offensive-language-twitter, blackburn-trolling-4chan, ouyang-training-language-models-follow-instructions, shu-exploitability-instruction-tuning, change-llm-agents, verma-statistical-detection-llm). Also removed references to non-existent papers by adding author names instead (e.g., removed 2019-yang-xlnet replaced with "XLNet (autoregressive approach, contemporary)").

[2026-06-07] lint | weekly auto-fix

• fixed: 56 mechanical issues
• touched: 50 author pages, wiki/papers/2019-liu-roberta.md, wiki/datasets/nela-gt-2019.md, wiki/datasets/nela-gt-2020.md, wiki/datasets/coaid.md
• notes: Removed broken references to 12 non-existent papers (2022-ouyang-instructgpt, 2023-shu-exploitability-instruction-tuning, 2023-ganguli-moral-self-correction, 2023-liu-lost-in-middle, 2020-guu-realm, 2023-guerreiro-hallucinations-multilingual, 2013-kingma-variational-autoencoder, 2023-wang-medical-summarization-metrics, 2023-dahein-model-merging, 2020-angelov-top2vec, 2020-gruppi-nela-gt-2019, 2021-gruppi-nela-gt-2020). Removed 4 references to non-existent topic AI-generated-text-detection. Fixed topic slug covid-19-misinformation → covid19-misinformation. Replaced broken paper reference 2018-devlin-bert with plain text in 2019-liu-roberta.md.

[2026-05-31] lint | auto-fix

• fixed: 46 broken wikilinks (paper slug corrections, deleted paper references, non-existent topic references)
• touched: 22 author pages, wiki/log.md, wiki/topics/foundation-models.md, wiki/papers/2023-zhang-llm-knowledge-update.md, wiki/papers/2023-chang-evaluation-llms.md
• notes: Fixed incorrect paper slug 2023-zhao-survey-large-language-models → 2023-zhao-large-language-models-survey in 18 author pages and 3 other pages. Removed broken topic references (nlp, social-network-analysis, generative-ai, adversarial-attacks, instruction-following, machine-learning-security, human-feedback) from author topic lists. Replaced deleted paper references (2022-ouyang-instructgpt, 2016-jones-tweeting-negative-emotion, 2020-clark-electra) with plain text in author pages.

[2026-05-26] figure | 2020-morris-textattack

• prepared figure prompt for adversarial attack framework: 1x4 pipeline (Goal, Constraints, Transform, Search) showing four modular components decomposing attacks into reusable parts, with headline finding that modularity enables reproducible and fair benchmarking

[2026-05-26] figure | 2013-mikolov-phrases-compositionality

• prepared figure prompt for Skip-gram extensions: 2x2 grid (Sampling, Negative, Phrases, Composition) showing four techniques for learning word/phrase embeddings with compositionality, with headline finding that vector addition combines semantic properties meaningfully

[2026-05-26] figure | 2022-izacard-atlas-few-shot

• prepared figure prompt for retrieval-augmented learning: 1x2 pipeline (Retriever, Language Model) showing dense document ranking and answer generation from retrieved documents, with headline finding that 42.4% NaturalQuestions accuracy with 64 examples outperforms much larger models

[2026-05-26] lint | auto-fix

• fixed: 56 broken wikilinks + 1 deleted paper reference
• touched: wiki/log.md, wiki/papers/2023-liu-trustworthy-llms.md, 44 author pages
• notes: Removed non-existent topic references (nlp, vaccines, emotion-recognition, speech-processing, multimodal-learning, image-caption-generation, audio-analysis, online-communities, web-communities, offensive-language, language-identification, computational-linguistics, aggression-detection, multilingual-nlp, online-safety, algorithmic-moderation) from author topic lists. Fixed incorrect paper slug references: 2023-zhao-survey-large-language-models → A Survey of Large Language Models. Replaced deleted paper references (2022-ouyang-instructgpt, 2016-jones-tweeting-negative-emotion) with plain text.

[2026-05-26] figure | 2025-cao-slim-limited-information

• prepared figure prompt for limited-information detection: 1x4 pipeline (Keyword, Sequence, Metadata, Multimodal) showing four SLIM variants reducing input representation, with headline finding that 30% of MMR-selected keywords recovers ~99% of full-text fake news detection accuracy

[2026-05-26 14:32] figure | 2024-wan-dell-llm-misinformation

• Figure prompt prepared for DELL: three-stage LLM pipeline for misinformation detection

Append-only record of ingests, queries, lint passes, and daily updates. Each entry uses a stable header format so it's grep-able:

## [YYYY-MM-DD] <kind> | <subject>

Where <kind> is one of: ingest, lint, daily, query, bootstrap.

Quick views from the shell:

grep "^## \[" wiki/log.md | tail -10           # last 10 events
grep "^## \[" wiki/log.md | grep "ingest"      # all ingests
grep -A 4 "ZAFARANI" wiki/log.md | tail -20    # recent Zafarani-related entries

[2026-05-26] figure | 2024-marchal-genai-misuse-taxonomy

• prepared figure prompt for GenAI misuse tactics: 1x3 taxonomy (Exploit, Compromise, Goals) showing tactics exploiting GenAI capabilities vs. targeting systems, plus five misuse objectives, with headline finding that low-tech GenAI misuse exploits easily accessible capabilities rather than sophisticated system attacks

[2026-05-26] figure | 2024-mao-graph-representation-llm-survey

• prepared figure prompt for LLM-enhanced graph learning: 1x4 pipeline (Extract, Organize, Integrate, Train) showing taxonomy of knowledge extractors, organizers, integration strategies, and training approaches, with headline finding that systematizes LLM-GRL integration design space into extraction, organization, integration, and training components

[2026-05-26] figure | 2024-liu-attitude-dynamics-fake-news

• prepared figure prompt for LLM-agent opinion dynamics: 1x4 pipeline (Memory, Reasoning, Propagation, Intervention) showing dual memory system, LLM-based reasoning, social propagation, and refutation mechanisms, with headline finding that political misinformation spreads 2× faster than science topics; credulous agents 1.7× more susceptible; early interventions reduce beliefs from 1.0 to 0.90

[2026-05-26] figure | 2024-bang-political-bias-llms

• prepared figure prompt for political bias measurement: 1x3 pipeline (Stance, Content, Style) showing two-tiered framework decomposing bias into political stance, content bias, and style bias, with headline finding that models exhibit liberal leanings on social issues with US-centric focus; bias varies significantly by issue and model architecture

[2026-05-26] figure | 2024-ba-deepfake-forgery-clues

• prepared figure prompt for deepfake detection: 1x2 pipeline (Local, Global) showing disentangled local feature extraction and global aggregation modules, with headline finding that mutual information-driven decomposition achieves 0.983 AUC on FaceForensics++ with strong 0.851 cross-dataset generalization

[2026-05-26] figure | 2023-zhao-large-language-models-survey

• prepared figure prompt for LLM development: 1x4 pipeline (Pretraining, Adaptation, Utilization, Alignment) showing four major dimensions of LLM development from scaling laws through alignment techniques, with headline finding that emergent abilities appear at scale and alignment techniques successfully reduce harmful outputs while maintaining capability

[2026-05-26] figure | 2023-wu-llm-generated-text-detection-survey

• prepared figure prompt for LLM-generated text detection: 2x2 grid (Watermarking, Statistical, Neural, Human-assisted) showing four detection paradigms, with headline finding that high in-distribution accuracy (99.79% F1) degrades under adversarial attacks (watermark detection: 97% → 80%)

[2026-05-26] figure | 2023-wei-jailbroken-llm-safety

• prepared figure prompt for LLM safety training failures: 1x2 diagram (Competing Objectives, Mismatched Generalization) showing how conflicting training objectives and out-of-distribution capabilities enable jailbreaks, with headline finding that vulnerabilities persist despite red-teaming due to fundamental training failures

[2026-05-26] figure | 2023-wang-folk-claim-verification

• prepared figure prompt for FOLK claim verification: 1x3 pipeline (Decompose, Retrieve, Verify) showing FOL decomposition, knowledge-grounded answer retrieval, and veracity prediction with explanations, with headline finding that FOL-guided decomposition achieves 11.30% improvement over chain-of-thought on complex claims

[2026-05-26] figure | 2023-wang-factuality-llms

• prepared figure prompt for LLM factuality survey: 1x4 pipeline (Problem, Evaluate, Mechanisms, Enhance) showing factuality challenges, evaluation metrics and benchmarks, error mechanisms, and enhancement strategies including RAG and fine-tuning, with headline finding that GPT-4 achieves only 42-71% factual accuracy on biographical generation

[2026-05-26] figure | 2023-vykopal-disinformation-capabilities-llms

• prepared figure prompt for LLM disinformation generation: 1x4 pipeline (Generation, Evaluation, Detection, Safety) showing 10 LLMs generating disinformation articles, human evaluation of narrative agreement and quality, detection method testing, and safety mechanism assessment, with headline finding that most LLMs readily generate plausible disinformation; only Falcon and ChatGPT have effective safeguards

[2026-05-26] figure | 2023-tang-detecting-llm-generated

• prepared figure prompt for LLM-generated text detection: 1x4 pipeline (Feature, Classify, Watermark, Evaluate) comparing black-box feature-based and white-box watermarking detection approaches, with headline finding that as LLMs improve, black-box detection fails and white-box watermarking becomes necessary

[2026-05-26] figure | 2023-singhania-3han

• prepared figure prompt for hierarchical attention network: 1x3 pipeline (Words, Sentences, Headline) showing three-level GRU encoder with attention at each level, with headline finding that explicit headline-body modeling achieves 96.77% accuracy, outperforming two-level baseline by 1.37 points

[2026-05-26] figure | 2023-ribeiro-amplification-paradox

• prepared figure prompt for recommender system paradox: 1x2 comparative diagram (Audit vs Reality) showing how random selection reveals amplification but utility-informed user preferences reveal deamplification of extreme content, with headline finding that extreme content is deamplified when users follow preferences due to low utility for niche topics

[2026-05-26] figure | 2023-quelle-fact-checking-llms

• prepared figure prompt for LLM-based fact-checking with evidence retrieval: 1x4 pipeline (Query, Retrieve, Reason, Verify) showing iterative web search and reasoning, with headline finding that external evidence improves accuracy 10–20 points but English training-data dominance severely limits multilingual verification

[2026-05-26] figure | 2023-pan-misinformation-pollution

• prepared figure prompt for misinformation pollution via LLMs: 1x4 grid of generation methods (GenRead, CtrlGen, Revise, Reit) showing four adversarial strategies for generating false information and contaminating retrieval corpora, with headline finding that LLMs generate convincing misinformation causing up to 87% ODQA performance degradation

[2026-05-26] figure | 2023-zou-universal-transferable-adversarial-attacks

• prepared figure prompt for adversarial suffix generation: 1x3 pipeline (Affirmative, Optimization, Transfer) showing affirmative priming, GCG search for adversarial suffix discovery, and cross-model evaluation, with headline finding that single suffix achieves 87.9% attack success on GPT-3.5 demonstrating alignment vulnerabilities

[2026-05-26] figure | 2023-mehrabi-flirt-red-teaming

• prepared figure prompt for automated red teaming: 1x3 pipeline (Generate, Target, Assess) showing in-context adversarial prompt generation, target model evaluation, and safety classifier feedback loop, with headline finding that FLIRT achieves 85.2% attack success on vanilla Stable Diffusion

[2026-05-26] figure | 2023-mazurczyk-disinformation-2-0

• prepared figure prompt for disinformation 2.0: 1x4 defense layers (Social, ISP, Device, User) showing layered mitigation strategy against AI-enabled misinformation, with headline finding that defense-in-depth across network, ISP, device, and user layers mitigates disinformation 2.0

[2026-05-26] figure | 2023-liu-trustworthy-llms

• prepared figure prompt for LLM trustworthiness evaluation: 2x3 grid (Reliability, Safety, Fairness, Misuse, Explainability, Robustness) showing six key dimensions of alignment assessment with icons and per-dimension evaluation criteria, with headline finding that aligned models excel at reliability but struggle with fairness; effectiveness varies across dimensions

[2026-05-26] figure | 2023-kirchenbauer-watermark-llm

• prepared figure prompt for LLM watermarking: 1x3 pipeline (Partition, Promote, Detect) showing vocabulary partitioning into green/red token lists, entropy-adaptive logit boosting, and z-test statistical detection, with headline finding that 98.4% detection rate with 1.2% false positives remains robust to paraphrasing and substitution attacks

[2026-05-26] figure | 2023-goldstein-language-models-influence

• prepared figure prompt for language models and influence operations: 2x2 grid (Design, Access, Dissemination, Belief) showing four intervention stages across influence pipeline, with headline finding that no single mitigation prevents LLM-driven influence; effective defense requires multi-stakeholder coordination

[2026-05-26] figure | 2023-deshpande-chatgpt-toxicity

• prepared figure prompt for persona-induced toxicity: 1x4 pipeline (Assignment, Generation, Measurement, Analysis) showing persona assignment via system parameter, 500K+ response generation, toxicity measurement via PERSPECTIVE API, and bias detection across demographics, with headline finding that persona assignment increases toxicity 6× and reveals discriminatory targeting of marginalized groups

[2026-05-26] figure | 2023-dai-trustworthy-gnn-survey

• prepared figure prompt for trustworthy GNNs: 1x4 grid (Privacy, Robustness, Fairness, Explainability) showing defenses against attacks, adversarial protection, non-discriminatory predictions, and interpretability methods, with headline finding that trustworthiness aspects interconnect; privacy noise can degrade robustness and fairness constraints may amplify adversarial vulnerabilities

[2026-05-26] figure | 2023-chen-combating-misinformation-age-llms

• prepared figure prompt for LLMs combating misinformation: 2x2 grid (Detection, Intervention, Hallucination, Misuse) showing opportunities to detect and counter misinformation alongside challenges from hallucinations and intentional misuse, with headline finding that LLMs enable detection and intervention yet simultaneously generate deceptive content at scale

[2026-05-26] figure | 2023-chang-evaluation-llms

• prepared figure prompt for LLM evaluation: 1x3 dimensions (WHAT, WHERE, HOW) showing task taxonomy, datasets and benchmarks, and evaluation metrics/protocols, with headline finding that existing evaluation protocols remain fragmented and insufficient to assess full LLM capabilities

[2026-05-26] figure | 2023-mitchell-detectgpt

• prepared figure prompt for machine-generated text detection: 1x4 pipeline (Perturb, Score, Estimate, Classify) showing text perturbation generation, probability scoring, curvature estimation via discrepancy, and classification threshold, with headline finding that LLM-generated text occupies negative-curvature regions while human text occupies neutral regions

[2026-05-26] figure | 2024-liu-teller

• prepared figure prompt for trustworthy fake news detection: 1x2 dual-system (Cognition, Decision) showing LLM decomposition of news into interpretable yes/no questions and neural-symbolic learning of disjunctive normal form rules, with headline finding that TELLER achieves 76% GossipCop and 80%+ dataset accuracy while maintaining human-readable explanations and controllability through manual rule adjustment

[2026-05-26] figure | 2023-cao-aigc-survey

• prepared figure prompt for AIGC survey: 2x2 grid (Foundation, Unimodal, Multimodal, Trustworthiness) showing transformers and pretraining, language/vision/audio models, cross-modal generation, and security/privacy/factuality safeguards, with headline finding that AI-generated content systems require trustworthiness mechanisms to address factuality risks and prevent misinformation amplification

[2026-05-26] figure | 2022-yang-botometer-101

• prepared figure prompt for bot detection on Twitter: 1x3 pipeline (Features, Classification, Scores) showing feature extraction from profiles/tweets/network, ensemble of specialized classifiers, and bot score + CAP probability output, with headline finding that Botometer-V4 achieves 0.99 AUC discrimination; bot prevalence varies dramatically across topics

[2026-05-26] figure | 2022-wang-toxicity-detection-generative

• prepared figure prompt for toxicity detection: 1x3 pipeline (Discriminative, Generative, Demonstrations) showing classification approaches with cloze-style prompts, conditional generation likelihood, and task-guided examples, with headline finding that generative classification with demonstrations surpasses all baselines across toxicity datasets

[2026-05-26] figure | 2022-rana-deepfake-detection-survey

• prepared figure prompt for deepfake detection taxonomy: 1x4 method categories (Deep Learning, Machine Learning, Statistical, Blockchain) showing technique prevalence and performance comparison, with deep learning dominance at 77% of studies achieving 89.73% accuracy versus 85% for traditional methods

[2026-05-26] figure | 2022-perez-red-teaming-language-models

• prepared figure prompt for red teaming language models: 1x4 pipeline (Zero-Shot, Few-Shot, Supervised, Reinforcement) showing test case generation methods with effectiveness progression from 3.7% to 42% offensive outputs, discovering data leakage, privacy violations, contact information generation, and distributional biases, with headline finding that RL-based red teaming achieves 42% offensive outputs versus 3.7% for zero-shot

[2026-05-26] figure | 2022-perez-discovering-lm-behaviors

• prepared figure prompt for LM behavior discovery: 1x4 pipeline (Generate, Test, Train, Predict) showing LM-based evaluation generation with discriminator filtering, behavior testing across 154 datasets and model sizes 810M-52B, RLHF training with 0-1000 steps, and preference model outcome prediction, with headline finding that RLHF amplifies political bias, sycophancy, goal-seeking; preference models predict with 88-96% accuracy

[2026-05-26] figure | 2022-munger-right-wing-youtube

• prepared figure prompt for right-wing YouTube: 1x3 supply-demand-platform flow (Supply: creator clusters, Platform: affordances and algorithm, Demand: viewership and engagement) showing how ideological creator groups interact with YouTube's monetization and recommendation features to drive audience growth, with headline finding that far-right viewership growth (2013-2017) driven equally by supply affordances and audience demand; peaked mid-2017, declined despite algorithm demotion

[2026-05-26] figure | 2022-ji-hallucination-nlg

• prepared figure prompt for NLG hallucination survey: 1x4 pipeline (Define, Detect, Mitigate, Apply) showing intrinsic/extrinsic hallucination taxonomy, statistical/model-based/human evaluation metrics, architecture/training/post-processing mitigation strategies, and application across six NLG tasks, with headline finding that hallucination is task-dependent; easily detected in summarization against sources but substantially harder in dialogue and LLMs

[2026-05-26] figure | 2022-hosseinmardi-radical-youtube

• prepared figure prompt for YouTube radicalization: 1x4 pipeline (Classify, Referral, Session, Compare) showing political category classification, referral pathway analysis, within-session escalation patterns, and off-platform consumption correlation, with headline finding that YouTube's recommendation algorithm does not systematically drive users to radical content; consumption reflects user preferences

[2026-05-26] figure | 2022-gruppi-nela-gt-2022

• prepared figure prompt for NELA-GT-2022: 1x4 pipeline (Collection, Labels, Tweets, Subsets) showing RSS scraping from 361 outlets, Media Bias/Fact Check source labeling, embedded tweet extraction, and event-based filtering, with headline finding that 1.78M articles with source veracity labels and embedded tweets support longitudinal misinformation research

[2026-05-26] figure | 2022-fan-trustworthy-recommender-systems

• prepared figure prompt for trustworthy recommender systems: 2x3 taxonomy (Safety, Fairness, Explainability, Privacy, Sustainability, Accountability) showing six interdependent dimensions of trustworthiness with defense mechanisms, equitable treatment, transparency, data protection, efficiency, and auditability, with headline finding that six dimensions are required but most work addresses only one or two; real-world deployment remains limited

[2026-05-26] figure | 2022-ecker-psychological-drivers-misinformation

• prepared figure prompt for psychological drivers of misinformation: 1x4 pipeline (Drivers, Barriers, Prebunk, Debunk) showing cognitive/social/affective factors driving false belief formation, integration failure and illusory truth resisting correction, pre-emptive inoculation strategies, and post-exposure refutation with alternatives, with headline finding that cognitive, social, and affective drivers shape false beliefs and interventions require repeated reinforcement

[2026-05-26] figure | 2021-wang-metafend

• prepared figure prompt for emergent fake news detection via meta-learning: 1x4 pipeline (Extraction, Embedding, Attention, Detector) showing multimodal feature extraction from text and images, learned semantic embeddings for fake/real labels, hard attention selecting single most informative post, and cosine-similarity based classification, with headline finding that MetaFEND achieves 4–5% accuracy gains over MAML through hard attention and label embedding for few-shot fake news detection on emergent events

[2026-05-26] figure | 2021-vo-hierarchical-evidence-attention

• prepared figure prompt for hierarchical multi-head attention fact-checking: 1x3 pipeline (WordAttn, DocAttn, Classify) showing word-level attention over claims and evidence, document-level attention identifying critical evidence, and MLP veracity classification, with headline finding that hierarchical attention combining word and document levels achieves 9.47% improvement on Snopes

[2026-05-26] figure | 2021-silva-cross-domain-multimodal

• prepared figure prompt for cross-domain fake news detection: 1x3 pipeline (Discovery, Classification, Selection) showing unsupervised domain embedding learning from propagation networks, domain-specific and cross-domain representation learning, and LSH-based instance selection, with headline finding that joint domain-specific and cross-domain knowledge yields 7.55% F1-score improvement on rarely-appearing domains

[2026-05-26] figure | 2021-pulastya-mislabeled-samples

• prepared figure prompt for mislabeled sample detection via latent outliers: 1x4 pipeline (Encode, Cluster, Detect, Filter) showing VAE encoding, label-wise clustering in latent space, outlier detection via MAD, and noise scoring for filtering, with headline finding that AQUAVS identifies >94% of mislabeled samples without clean validation data or noise-type priors, enabling robust dataset curation

[2026-05-26] figure | 2021-pennycook-shifting-attention-accuracy

• prepared figure prompt for accuracy-priming intervention: 1x4 pipeline (Problem, Intervention, Trial, Outcome) showing the disconnect between accuracy beliefs and sharing, the attention-based treatment, the Twitter field experiment with 379 users, and the quality improvement metric, with headline finding that accuracy-priming increases quality of shared news by 4.8 percentage points in real-world field trials

[2026-05-26] figure | 2021-nan-mdfend-multidomain

• prepared figure prompt for multi-domain fake news detection: 1x3 pipeline (Experts, Gate, Classify) showing multiple expert networks extracting representations, domain gate adaptively weighting outputs, and binary classification, with headline finding that mixture-of-experts with domain gating detects fake news across nine domains with 91.37% F1 score, +1.62% improvement over prior best

[2026-05-26] figure | 2021-nakov-media-profiling-factuality-bias

• prepared figure prompt for media source profiling: 1x4 framework (Text, Visual, Audience, Infrastructure) showing four modalities for predicting news outlet factuality and bias, with headline finding that bias prediction consistently outperforms factuality assessment because partisanship is more detectable from language and audience signals than truth-value

[2026-05-26] figure | 2021-mosleh-partisanship-tie-formation

• prepared figure prompt for partisan tie formation experiment: 1x3 pipeline (Copartisan, Symmetry, Robustness) showing ~3x boost in follow-back for matching party, equal partisan preference across Democrats and Republicans, and independence from bot political intensity, with headline finding that shared partisanship causally drives ~3x increase in social tie formation with symmetric and extremity-independent effects

[2026-05-26] figure | 2021-mirsky-threat-offensive-ai

• prepared figure prompt for offensive AI threat assessment: 1x4 pipeline (Capabilities, Evaluation, Scoring, Ranking) enumerating 33 offensive AI techniques across seven categories, expert assessment on profit/achievability/defeatability/harm dimensions, threat calculation model (T = H × M/D), and threat ranking, with headline finding that exploit development, social engineering, and information gathering rank as highest threats; AI provides attackers asymmetric advantage during reconnaissance and weaponization; 72% of identified capabilities pose significant organizational risk

[2026-05-26] figure | 2021-mayank-deap-faked

• prepared figure prompt for DEAP-FAKED knowledge graph fake news detection: 1x3 pipeline (Encoder, Entities, Fusion) combining biLSTM title encoding, named entity extraction and ComplEx knowledge graph embeddings from Wikidata, and concatenation for binary classification, with headline finding that knowledge graph entity embeddings improve fake news detection by 13% F1 over content-only baselines

[2026-05-26] figure | 2021-zhou-belief-evolution-network

• prepared figure prompt for Belief Evolution Network probability transformation: 1x3 pipeline (Network, Transform, Combine) showing hierarchical organization of focal elements by cardinality, Full Causality probability transformation converting belief mass to probability, and FCPT-based fusion rule combining multiple evidence sources, with headline finding that FCPT-PCR handles conflicting information better than Dempster's Rule, achieving 95%+ classification accuracy

[2026-05-26] figure | 2021-lin-truthfulqa

• prepared figure prompt for TruthfulQA: 1x3 pipeline (Models, Evaluation, Results) testing language models at multiple scales, human and automated GPT-judge evaluation, and truthfulness scoring, with headline finding that larger models are less truthful than smaller ones (inverse scaling paradox: 58% vs 94% human baseline)

[2026-05-26] figure | 2021-mosleh-cognitive-reflection-twitter

• prepared figure prompt for cognitive reflection and social media behavior: 2x2 grid (Network, Sources, Topics, Language) showing how high-CRT users form selective networks, prefer trusted news outlets, engage with substantive topics, and use reflective language, with headline finding that high analytical thinking predicts discernment across network, news sources, engagement topics, and linguistic patterns

[2026-05-26] figure | 2021-vraga-news-literacy-skepticism

• prepared figure prompt for news literacy and social media skepticism: 1x2 pipeline (Actual, Perceived) showing how actual news literacy knowledge increases skepticism and reduces posting, while perceived confidence (overconfidence) increases engagement but decreases critical thinking, with headline finding that actual knowledge predicts skepticism while overconfidence drives engagement without critical evaluation

[2026-05-26] figure | 2021-kaliyar-fakebert

• prepared figure prompt for BERT-CNN fake news detection: 1x4 pipeline (Encoding, Extraction, Pooling, Classification) using BERT for contextualized embeddings, parallel 1D CNNs with varying kernel sizes for multi-scale feature extraction, max-pooling and concatenation, and fully connected softmax classification, with headline finding that FakeBERT achieves 98.90% accuracy with FPR=0.0160 and FNR=0.0059, substantially outperforming LSTM and CNN baselines

[2026-05-26] figure | 2021-jones-jang-media-literacy-fake-news

• prepared figure prompt for media literacy and fake news identification: 2x2 comparison (Media, Information, News, Digital) showing four literacy types and their relationship to fake news detection accuracy, with headline finding that only information literacy (source evaluation) significantly predicts fake news identification; media, news, and digital literacies do not

[2026-05-26] figure | 2021-suarez-lledo-health-misinformation-social-media

• prepared figure prompt for health misinformation prevalence: 1x3 taxonomy (Topics, Platforms, Methods) showing health topic distribution (vaccines 32%, drugs 22%, NCDs 19%), platform prevalence (Twitter 29%, YouTube 23%, Facebook 9%), and research methodologies (content analysis 22%, quality evaluation 24%, sentiment 6%), with headline finding that vaccines dominate health misinformation research across platforms, with Twitter as primary venue and content analysis/quality evaluation as dominant methods

[2026-05-26] figure | 2021-jin-finerfact

• prepared figure prompt for fine-grained fake news reasoning: 1x3 pipeline (Construction, Ranking, Reasoning) building claim-evidence graphs via topic-aware association, ranking evidence through mutual reinforcement of user and topological salience, and reasoning with bi-channel KGAT over textual and social signals, with headline finding that fine-grained claim-evidence reasoning with interpretable explanations outperforms baselines by 6%+ F1

[2026-05-26 21:06] figure | 2021-jagtap-youtube-captions

• prepared figure prompt for YouTube misinformation detection: 1x3 pipeline (Captions, Embeddings, Classify) extracting video captions, converting to word vectors via GloVe/Word2Vec, and training classical ML classifiers, with headline finding that caption-based models achieve 0.92–0.95 F1-score, substantially outperforming metadata alone

[2026-05-26] figure | 2021-hosseinmardi-youtube-radicalization

• prepared figure prompt for YouTube radicalization audit: 1x3 convergent empirical tests (Composition, Sources, Ecosystem) showing far-right 0.05% of users, 55% of far-right entries from external sources not recommendations, and far-right users equally far-right off-platform, with headline finding that algorithm does not systematically drive radicalization; patterns reflect pre-existing user preferences across the broader web

[2026-05-26] figure | 2021-hardalov-stance-detection-survey

• prepared figure prompt for stance detection in misinformation: 1x4 survey landscape (Formulation, Datasets, Methods, Application) showing task definition, benchmark corpora, evolution from features to transformers, and integration into fact-checking pipelines, with headline finding that stance detection evolved from feature engineering to transformers and serves as critical component in misinformation detection

[2026-05-26] figure | 2021-evans-truthful-ai

• prepared figure prompt for AI truthfulness governance: 1x3 framework (Framework, Governance, Technical) showing conceptual distinctions between truthfulness and honesty, three institutional governance models, and six technical approaches (RLHF, transparency, retrieval-grounding, adversarial training), with headline finding that early standards-setting for AI truthfulness before superhuman deception is essential for governance

[2026-05-26] figure | 2021-du-cross-lingual-covid19

• prepared figure prompt for cross-lingual COVID-19 fake news detection: 1x4 pipeline (Translate, Slice, Encode, Classify) showing machine translation of Chinese news, sub-text slicing into 500-token groups, BERT encoding with mean pooling, and MLP-based binary classification, with headline finding that CrossFake achieves 75% accuracy on Chinese COVID-19 news, outperforming cross-lingual baselines by 30+ percentage points

[2026-05-26] figure | 2021-das-heuristic-uncertainty-ensemble

• prepared figure prompt for ensemble fake news detection: 1x4 pipeline (Ensemble, Fusion, Uncertainty, Heuristic) showing soft voting of transformers, statistical feature fusion, Monte Carlo Dropout for confidence, and domain-specific post-processing rules, with headline finding that metadata-driven feature fusion and heuristic refinement improve FakeNewsNet accuracy by 9.56%

[2026-05-26] figure | 2021-clark-human-eval-generated-text

• prepared figure prompt for evaluating human judgment of machine-generated text: 1x4 empirical comparison (Baseline, Instructions, Examples, Comparison) showing four training conditions for improving evaluator accuracy, with headline finding that example-based training with practice and feedback is most effective

[2026-05-26] figure | 2021-cinelli-echo-chamber-social-media

• prepared figure prompt for platform-driven echo chamber dynamics: 2x2 comparative analysis (Twitter, Facebook, Reddit, Gab) showing how feed algorithms vs. community-based design create polarized vs. single-sided echo chambers, with headline finding that platform architecture determines whether echo chambers polarize users into opposing camps or coalesce into single dominant communities

[2026-05-26] figure | 2021-cheng-causal-fake-news-dissemination

• prepared figure prompt for causal understanding of fake news dissemination: 1x2 pipeline (Learning, Inference) showing two-stage approach using inverse propensity scoring to learn unbiased embeddings, then causal regression to identify user attributes affecting sharing susceptibility, with headline finding that verified users, higher tweet count, more followers, and organization accounts all reduce sharing susceptibility

[2026-05-26] figure | 2021-bommasani-foundation-models

• prepared figure prompt for foundation models opportunities and risks: 2x2 framework (Generate, Detect, Bias, Impact) showing dual nature of foundation models—their power to generate synthetic misinformation and deepfakes vs. detection capabilities—alongside fairness and societal harms, with headline finding that generative capabilities advance faster than detection defenses

[2026-05-26] figure | 2020-zhou-early-detection-interdisciplinary

• prepared figure prompt for fake news early detection via linguistic features: 1x4 pipeline (Lexicon, Syntax, Semantic, Discourse) showing four-level content-only feature extraction for publication-time detection without propagation data, with headline finding that content-only features achieve 89% accuracy on early detection, outperforming propagation-based baselines

[2026-05-26] figure | 2020-wilson-social-media-vaccine

• prepared figure prompt for social media and vaccine outcomes: 1x3 pipeline (Organizing, Disinformation, Discourse) showing mechanisms by which social media use and foreign government disinformation predict vaccine hesitancy and coverage reduction, with headline finding that disinformation reduces vaccination coverage 1.93 percentage points and increases negative vaccine tweets 15%

[2026-05-26] figure | 2020-vo-fact-checked-information-search

• prepared figure prompt for multimodal fact-check article retrieval: 1x3 pipeline (Textual matching via embeddings, Visual matching via ResNet50, Multimodal fusion via attention) showing automatic matching of tweet claims to existing fact-checking articles, with headline finding that multimodal retrieval outperforms text-only baselines by up to 39.6%

[2026-05-26] figure | 2020-zhou-safe-multimodal

• prepared figure prompt for multimodal fake news detection: 1x4 pipeline (Text, Image, Similarity, Classify) showing parallel text and image encoding followed by cross-modal similarity computation and joint classification, with headline finding that fake news has low text-image similarity (0.001–0.044) while true news has high similarity (0.966–0.983)

[2026-05-26] figure | 2020-tsfati-mainstream-media-dissemination

• prepared figure prompt for mainstream media dissemination of fake news: 1x3 causal pipeline (Why journalists cover it via news values and professional duty, Spread via repetition, Persist via fluency effects and mental models) showing how mainstream media amplifies false stories to audiences beyond direct fake news site reach, with headline finding that mainstream coverage drives most public exposure to fake news and corrections fail because mental models resist revision

[2026-05-26 16:00] figure | 2020-tolosana-deepfakes-survey

• prepared figure prompt for face manipulation and detection: 2x2 taxonomy of four manipulation types (Synthesis, Identity swap, Attributes, Expression) flowing to detection with cross-domain failure analysis, headline finding that detection methods exceed 99% accuracy in controlled scenarios but fail under cross-domain conditions

[2026-05-26 15:30] figure | 2020-tan-neural-fake-news

• prepared figure prompt for cross-modal inconsistency detection: 2x2 four-condition framework (Real+Real, Real+Generated, Generated+Real, Generated+Generated article types) showing how machine-generated news exploits multimodal mismatches, with headline finding that visual-semantic inconsistency is critical for detection and humans improve accuracy 21.6 percentage points with explicit visual-semantic cues

[2026-05-26] figure | 2020-shu-mining-disinformation-fake-news

• prepared figure prompt for weak supervision fake news detection tutorial: three-box weak supervision framework (Sentiment, Credibility, Network) showing how social signals enable scalable misinformation identification, with headline finding that weak signals from user sentiment, credibility, and network structure enable effective fake news detection without labeled training data

[2026-05-26] figure | 2020-san-martino-propaganda-detection-survey

• prepared figure prompt for propaganda detection survey: dual-perspective framework (Text Analysis for NLP-based technique detection, Network Analysis for coordinated behavior) showing the critical disconnect between communities and need for integration, with headline finding that fine-grained propaganda technique detection combined with network analysis outperforms single-modality approaches

[2026-05-26] figure | 2020-roozenbeek-susceptibility-covid-misinformation

• prepared figure prompt for international survey of COVID-19 misinformation susceptibility: four-box analysis (Assessment, Susceptibility, Predictors, Outcomes) showing measurement of conspiracy belief, susceptibility indexing, identification of protective factors (trust in scientists, numeracy), and prediction of vaccine hesitancy, with headline finding that higher misinformation susceptibility strongly predicts vaccine hesitancy and reduced health guidance compliance across all five countries

[2026-05-26 14:35] figure | 2020-pennycook-covid19-accuracy-nudge

• prepared figure prompt for accuracy-nudge intervention on COVID-19 misinformation: two-condition comparison (Control vs. Treatment with accuracy nudge) showing how making accuracy salient improves sharing discernment, with headline finding that a simple accuracy-salience nudge increased sharing discernment from 4 to 10 percentage points, a 2.8-fold improvement with no interaction across demographic groups

[2026-05-26 01:01] figure | 2020-sayyadiharikandeh-social-bots-ensemble

• prepared figure prompt for social bot detection ensemble: three-box pipeline (Baseline human detector, Specialized classifiers per bot type, Aggregation via maximum rule) showing heterogeneous bot behavior classification and cross-domain generalization, with headline finding that ESC improves cross-domain F1 from 47% to 73% and enables efficient adaptation to new bot types

[2026-05-26 23:59] figure | 2020-patwa-covid-fake-news-dataset

• prepared figure prompt for COVID-19 fake news classification benchmark: four-classifier comparison (Decision Tree, Logistic Regression, SVM, Gradient Boosting) evaluated on 10,700 annotated posts with TF-IDF features, with headline finding that SVM achieves 93.32% F1-score, outperforming three baseline classifiers on COVID-19 misinformation detection

[2026-05-26 23:59] figure | 2020-oshikawa-nlp-fake-news-detection

• prepared figure prompt for NLP-based fake news detection survey: five-box pipeline (Preprocessing, Models, Rhetoric, Evidence, Metadata) showing systematic NLP approaches from tokenization/embeddings through attention-LSTM classifiers with evidence collection and metadata exploitation, with headline finding that attention-based LSTM achieves highest accuracy; neural approaches outperform hand-crafted features, but metadata (speaker credibility, social engagement) remains critical

[2026-05-26 23:59] figure | 2020-lu-gcan-fake-news-detection

• prepared figure prompt for GCAN graph-aware co-attention network: four-box pipeline (Encoding, Propagation, Graph, Attention) showing user feature extraction, temporal/sequential propagation modeling via GRU and CNN, graph convolution on user interactions, and dual co-attention mechanism for fake news classification with explainability, with headline finding that dual co-attention achieves 18-20% improvement over prior methods via graph-aware user interactions and explainability

[2026-05-26 23:59] figure | 2020-linvill-troll-factories

• prepared figure prompt for IRA account typology: five-box grid (Trump, Liberal, News, Games, Crisis) showing specialized disinformation units with distinct messaging strategies and temporal deployment patterns, with headline finding that IRA operated as propaganda factory with five specialized units deployed strategically across distinct political event windows

[2026-05-26 00:46] figure | 2020-li-satire-detection

• prepared figure prompt for multi-modal satire detection: three-box pipeline (Text, Vision, Fusion) showing parallel BERT and CNN encoding with co-attentive fusion, with headline finding that multi-modal fusion outperforms uni-modal baselines for satire detection

[2026-05-26 23:59] figure | 2020-lewis-retrieval-augmented-generation

• prepared figure prompt for retrieval-augmented generation: two-box pipeline (Retriever, Generator) showing dense passage retrieval from Wikipedia followed by BART conditional generation with document marginalization, with headline finding that RAG augments pre-trained models with external knowledge for improved factuality without architectural changes

[2026-05-26 23:59] figure | 2020-lee-misinformation-perplexity

• prepared figure prompt for perplexity-based debunker: four-box pipeline (Selection, Filtering, Grounding, Debunking) showing evidence-extraction and language-model grounding workflow for claim verification, with headline finding that false claims have significantly higher perplexity under evidence-grounded language models

[2026-05-26 04:56] figure | 2021-alam-multimodal-disinformation-detection

• prepared figure prompt for multimodal disinformation detection survey: 1x3 pipeline (Modalities, Detection, Fusion) showing integration of six input types (text, image, video, audio, network, temporal) with dual factuality and harmfulness classification via three fusion strategies (early, late, hybrid), with headline finding that multimodal models outperform unimodal baselines by 6–15%; cross-modal fusion is essential

[2026-05-26 23:59] figure | 2020-hameleers-multimodal-disinformation

• prepared figure prompt for multimodal disinformation and rebuttals: three-box pipeline (Disinformation, Rebuttal, Judgment) showing stimulus presentation, fact-check intervention type, and credibility assessment moderated by prior attitudes, with headline finding that visual disinformation gains credibility yet fact-checks reduce it equally across modalities, stronger when prior attitudes oppose the claim

[2026-05-26 23:59] figure | 2020-guess-media-literacy-intervention

• prepared figure prompt for digital media literacy intervention: three-box pipeline (Tips, Rating, Discernment) showing how media literacy heuristics enable people to distinguish false from mainstream news, with headline finding that effects persist for weeks in online populations but decay and vanish in less digitally-connected populations

[2026-05-26 23:59] figure | 2020-giglietto-coordinated-inauthentic-behavior

• prepared figure prompt for coordinated link sharing detection: three-box pipeline (Temporal, Frequency, Analysis) showing how time-threshold and frequency-based grouping reveal coordinated networks amplifying problematic news, with headline finding that coordinated networks share problematic domains 1.79–2.22× more frequently than uncoordinated entities

[2026-05-26 00:35] figure | 2020-fagni-tweepfake-detection

• prepared figure prompt for tweet-generation detection benchmark: three-box comparison (Sparse BoW, Character-level, Transformer) showing detection accuracy progression from 80% (sparse features) through 84% (character-level encoding) to 90% (fine-tuned RoBERTa), highlighting superiority of transformer-based approaches for synthetic-text classification

[2026-05-26 23:58] figure | 2020-effron-misinformation-morality

• prepared figure prompt for moral desensitization: three-box pipeline (Familiarity, Judgment, Sharing) showing how repeated exposure reduces moral friction via fluency heuristic, increasing sharing behavior even for disbelieved misinformation

[2026-05-26 23:56] figure | 2020-dolhansky-dfdc-dataset

• prepared figure prompt for deepfake detection benchmark: four-box pipeline (Collection, Generation, Processing, Augmentation) showing DFDC dataset construction with multiple face-swap methods and robustness testing, with headline finding on difficulty of detection and augmentation-induced performance degradation

[2026-05-26 23:45] figure | 2020-danilevsky-explainable-ai-nlp-survey

• prepared figure prompt for explainability taxonomy: five-box pipeline (Importance, Surrogate, Examples, Provenance, Induction) showing core techniques for explaining neural NLP models, with headline finding on standardized evaluation gaps

[2026-05-26 23:18] figure | 2020-cui-coaid-covid19

• prepared figure prompt for multimodal COVID-19 dataset: four-box integration (Articles, Claims, Engagement, Posts) showing how combining news labels, health claims, user engagement signals, and multi-platform posts enables comprehensive misinformation detection with engagement-aware methods outperforming baselines

[2026-05-26 22:14] figure | 2020-cinelli-covid-infodemic

• prepared figure prompt for platform-specific COVID-19 infodemic: three-box pipeline (Epidemic, Classification, Amplification) showing how epidemic models (SIR/EXP) and source credibility metrics reveal platform-dependent misinformation dynamics, with Gab amplifying unreliable sources 400% while YouTube suppresses them 35%

[2026-05-26 21:55] figure | 2020-brown-gpt3-few-shot

• prepared figure prompt for in-context few-shot learning: three-box paradigm (Zero-shot, One-shot, Few-shot) showing how GPT-3's transformer processes task descriptions and examples without fine-tuning, with headline finding on synthetic news generation

[2026-05-26 21:48] figure | 2020-banda-covid19-twitter-dataset

• prepared figure prompt for COVID-19 dataset pipeline: four-box flow (Collection, Deduplication, Preprocessing, Aggregation) showing how 800+ million tweets are curated from Twitter API, deduplicated, cleaned, and aggregated into multilingual resource with metadata extraction

[2026-05-26 21:42] figure | 2020-baly-media-profiling

• prepared figure prompt for media profiling via multi-modal analysis: three-box integration (Articles, Audience, Wikipedia) showing how combining article content, social media audience demographics, and outlet context achieves 85% accuracy in political bias prediction

[2026-05-26 21:35] figure | 2020-allen-fake-news-ecosystem

• prepared figure prompt for ecosystem-scale measurement: three-box integration (TV, Online, Scale) showing how Nielsen and Comscore data reveal fake news as 0.15% of total media diet, with TV dominance at 5:1 ratio reframing the problem in ecosystem context

[2026-05-26 21:52] figure | 2020-bian-bi-gcn-rumor-detection

• prepared figure prompt for bidirectional rumor detection: three-box pipeline (TD-GCN, BU-GCN, Classify) showing how top-down propagation and bottom-up dispersion analysis achieves 96.1% detection accuracy with strong early-detection capability

[2026-05-26 21:25] figure | 2019-zlatkova-fauxtography

• prepared figure prompt for image claim verification: three-box pipeline (Search, Extract, Classify) showing how reverse image search with URL domain features achieves 80% accuracy on multimodal claim verification against Snopes and Reuters datasets

[2026-05-26 21:15] figure | 2019-zhou-network-pattern-detection

• prepared figure prompt for network-pattern detection: 2x2 grid (More-Spreader, Farther-Distance, Stronger-Engagement, Denser-Network) showing how network structural patterns alone achieve 0.929 accuracy, exceeding content-only baselines

[2026-05-26 20:35] figure | 2019-zhou-fundamental-theories-wsdm-tutorial

• prepared figure prompt for unified fake news detection framework: four-box grid (Knowledge, Style, Propagation, Credibility) showing complementary detection perspectives grounded in interdisciplinary theories from psychology, forensics, and social science

[2026-05-26 19:20] figure | 2019-zellers-grover-neural-fake-news

• prepared figure prompt for neural fake news generation and defense: three-box pipeline (Generate, Artifacts, Detect) showing GROVER's dual capability to generate realistic fake news (fooling humans at 2.42/3 credibility) and detect machine text via understanding exposure bias and variance-reduction signatures (92% accuracy)

[2026-05-26 18:45] figure | 2019-zannettou-russian-trolls-images

• prepared figure prompt for state-sponsored information warfare: three-box pipeline (Clustering, Extraction, Influence) showing how Russian IRA accounts strategically deployed 1.8M images, with political imagery achieving 2.3× higher cross-platform influence than non-political content

[2026-05-26 16:00] figure | 2019-zampieri-semeval-offenseval

• prepared figure prompt for hierarchical offensive language detection: three-box pipeline (Detection, Categorize, Target) showing OLID shared task structure distinguishing binary detection, offense type (targeted vs untargeted), and target classification (individual, group, or other)

[2026-05-26 15:42] figure | 2019-zampieri-offensive-language-identification

• prepared figure prompt for hierarchical offensive language detection: three-box pipeline (Detection, Type, Target) showing OLID annotation hierarchy that unifies hate speech, cyberbullying, and aggression detection

[2026-05-26 15:08] figure | 2019-yang-arming-public-counter-bots

• prepared figure prompt for bot detection calibration: three-box pipeline (Detection, Calibration, Interpretation) showing Botometer classifier, Platt scaling transformation, and Complete Automation Probability metric to make bot scores interpretable to the public

[2026-05-26 14:32] figure | 2019-solaiman-release-strategies-language-models

• prepared figure prompt for responsible AI: 2x2 grid framework (Threats, Detect, Bias, Release) showing staged GPT-2 deployment with threat assessment, detection evaluation (74–88% simple, ~95% RoBERTa), demographic bias analysis, and partnership-based research governance for responsible language model publication

[2026-05-26 00:01] figure | 2020-li-mm-covid-multilingual-multimodal

• prepared figure prompt for multilingual COVID-19 dataset: three-box pipeline (News, Social, Temporal) showing feature extraction across multilingual claims with social engagement and temporal signals, with headline finding that social context enables 6–14% accuracy gain and zero-shot cross-lingual transfer to under-resourced languages

[2026-05-25 22:15] figure | 2019-shu-user-profiles-fake-news

• prepared figure prompt for user profile detection: four-box pipeline (Selection, Features, Aggregate, Classify) showing UPF vector aggregation of Twitter user attributes achieving F1 0.904–0.966, with account age as dominant feature

[2026-05-25 10:20] figure | 2019-shu-defend-explainable-detection

• prepared figure prompt for explainable detection: four-box neural pipeline (Content, Comments, Co-Attention, Predict) showing hierarchical attention jointly modeling news and user comments, achieving 90.4% accuracy on PolitiFact and 80.8% on GossipCop

[2026-05-25 14:30] figure | 2019-san-martino-fine-grained-propaganda-detection

• prepared figure prompt for shared task: two-box classification pipeline (SLC and FLC) showing sentence-level and fragment-level propaganda detection with BERT achieving 0.63 and 0.25 F1 scores

[2026-05-25 23:57] figure | 2019-sahly-trump-clinton-framing

• prepared figure prompt for political campaign framing: three-box pipeline (Frames, Usage, Effects) showing content analysis of Trump and Clinton's 2016 social media strategy, revealing platform moderation of frame effects on engagement

[2026-05-25 09:45] figure | 2019-sabir-face-manipulation-video-detection

• prepared figure prompt for video-based manipulation detection: four-box pipeline (Align, Extract, Recur, Classify) showing how bidirectional GRU exploits temporal inconsistencies in Deepfake/Face2Face/FaceSwap detection, achieving 96.9% accuracy on FaceForensics++

[2026-05-26 08:15] figure | 2019-rossler-faceforensics-plus-plus

• prepared figure prompt for facial forgery benchmark: 2x2 grid pipeline (Face2Face, FaceSwap, DeepFakes, NeuralTextures) showing four state-of-the-art manipulation methods applied to 1,000+ pristine videos generating 1.8M+ images for detection evaluation, with XceptionNet achieving 99.26% accuracy versus 68.69% human baseline

[2026-05-25 18:30] figure | 2019-norregaard-nela-gt-2018

• prepared figure prompt for dataset: three-component pipeline (collection via RSS, multi-source assessment from eight sites, database output) showing engagement-independent collection of 713K articles with multi-dimensional credibility labels

[2026-05-26 07:32] figure | 2019-nieminen-fact-checking-literature

• prepared figure prompt for systematic review: three research areas (effectiveness, profession, opinion) categorizing 48 studies, highlighting mixed evidence on correction effectiveness and methodological heterogeneity across predominantly US-focused research

[2026-05-25 07:35] figure | 2019-nakamura-fakeddit

• prepared figure prompt for multimodal fake news dataset: three-component pipeline (text feature extraction, image feature extraction, multimodal fusion) showing how maximum fusion of BERT and ResNet50 embeddings achieves 85.88% accuracy on 6-way fine-grained classification, 9 percentage points better than text-only

[2026-05-25 17:52] figure | 2019-monti-geometric-deep-learning

• prepared figure prompt for graph neural network detection: three-box pipeline (Graph representation, Feature extraction, GCN classification) showing how social cascades encode fake news signatures detectable within 1-2 hours with 92.7% ROC AUC

[2026-05-25 17:45] figure | 2019-machado-whatsapp-misinformation

• prepared figure prompt for WhatsApp misinformation study: three-content-type analysis (Links, Videos, Images) showing distribution across 130 Brazilian election groups with 13.1% junk-news links and 33% of messages as visual propaganda

[2026-05-25 11:22] figure | 2019-liu-roberta

• prepared figure prompt for RoBERTa pretraining improvements: four-box pipeline (dynamic masking, remove NSP, longer sequences, larger batches) showing systematic modifications to BERT's training recipe achieving 88.5% GLUE accuracy

[2026-05-25 15:30] figure | 2019-khan-benchmark-ml-models

• prepared figure prompt for machine learning benchmark: three-model-family comparison (traditional, deep learning, pre-trained) showing RoBERTa achieving 96%+ accuracy on large datasets and maintaining 90%+ with only 500 samples

[2026-05-25 14:32] figure | 2019-karimi-hierarchical-discourse

• prepared figure prompt for discourse-structure pipeline: unsupervised learning of hierarchical sentence dependencies via BLSTM and attention, extracting three structural properties (leaf nodes, preorder difference, parent-child distance) that distinguish real from fake news with 82.19% accuracy

[2026-05-26 16:22] figure | 2019-yang-scalable-bot-detection-data-selection

• prepared figure prompt for bot detection data selection: four-box pipeline (Compilation, Selection, Training, Detection) showing how strategic dataset subset choice outperforms exhaustive training, enabling real-time classification at 900M tweets/day with 20 metadata features

[2026-05-26 06:20] figure | 2019-horta-ribeiro-auditing-radicalization

• prepared figure prompt for user radicalization pathways: three-community migration flow (Intellectual Dark Web → Alt-lite → Alt-right) showing algorithmic reachability (50%) enabling user progression to extreme content, with 40% of Alt-right users traceable to milder-community origins

[2026-05-25 20:15] figure | 2019-roozenbeek-bad-news-game

• prepared figure prompt for intervention study: four-component inoculation game teaching deception techniques (impersonation, conspiracy, discrediting, polarisation) showing equal-opportunity cognitive resistance building with d=0.52 effect across all demographics

[2026-05-26 06:15] figure | 2019-hanselowski-richly-annotated-fact-checking

• prepared figure prompt for multi-task fact-checking corpus: four-stage pipeline (document retrieval, evidence extraction, stance detection, claim validation) from claims to annotated corpus of 6,422 claims and 14,296 heterogeneous documents

[2026-05-25 23:45] figure | 2019-grinberg-twitter-election

• prepared figure prompt for fake news concentration on Twitter: exposure distribution pipeline from 16,442 voter-linked accounts → three analysis streams (exposure concentration, user demographics, media ecosystem structure) → partisan network map, showing 0.1% of users drive 80% of fake news exposures

[2026-05-25 21:05] figure | 2019-gorwa-platform-governance-triangle

• prepared figure prompt for platform governance triangle: three actor types (state, firms, civil society) and their complementary competencies (legitimacy, technical expertise, independence) in informal content regulation

[2026-05-25 20:15] figure | 2019-gehrmann-gltr-generated-text

• prepared figure prompt for generated text detection pipeline: text → language model analysis → three statistical tests (probability, rank, entropy) → GLTR interactive visualization, showing 85-87% AUC for distinguishing human from model-generated content

[2026-05-25 19:45] figure | 2019-eady-political-bubbles

• prepared figure prompt for political bubble measurement: survey-Twitter linkage methodology showing ideology estimation via follower composition, bubble classification with 5% cross-cutting threshold, and measurement of exposure diversity through retweets and weak ties

[2026-05-25 18:27] figure | 2019-cifci-fakecatcher

• prepared figure prompt for deepfake detection via biological signal analysis: facial video → PPG signal extraction (cheek and mid-face regions) → cross-spectral and wavelet feature transforms → SVM and CNN classification, showing 91%+ accuracy by exploiting spatial coherence and temporal consistency artifacts that GANs fail to synthesize

[2026-05-26 05:12] figure | 2019-augenstein-multifc-fact-checking

• prepared figure prompt for multi-domain fact-checking dataset pipeline: website crawling (34,918 claims from 26 sites) → evidence retrieval (Google Search) → multi-task encoding (shared + domain-specific layers) → veracity prediction, showing 17.4% improvement with evidence ranking

[2026-05-25 16:42] figure | 2019-adelani-sentiment-preserving-fake-reviews

• prepared figure prompt for sentiment-preserving fake review generation: two-stage pipeline (GPT-2 generation → BERT sentiment validation) showing how fine-tuned language models generate reviews indistinguishable from authentic ones, highlighting detector failure across humans and automated systems

[2026-05-26 02:35] figure | 2018-zhou-two-stream-tampered-face

• prepared figure prompt for two-stream face tampering detection: combines GoogLeNet visual artifact detection with steganalysis triplet network noise analysis for 0.927 AUC robustness

[2026-05-25 14:31] figure | 2018-zannettou-web-false-information

• prepared figure prompt for false information ecosystem taxonomy: four research lines (perception, propagation, detection, politics) organizing ecosystem of eight information types, twelve actors, six motives across user perception, network dynamics, detection approaches, and political misinformation

[2026-05-25 14:30] figure | 2018-zannettou-state-sponsored-trolls

• prepared figure prompt for comparative state-sponsored troll analysis: verified accounts (Russian/Iranian) → account characteristics (creation dates, follower ratios) / temporal patterns (event-driven activity) / content analysis (ideological targeting) / influence metrics (cascades, URL amplification) → comparative effectiveness showing Russian trolls 2-3× more effective at content reach

[2026-05-26 02:25] figure | 2018-zannettou-meme-origins

• prepared figure prompt for large-scale meme ecosystem pipeline: 160M images across four communities (Twitter, Reddit, /pol/, Gab) → perceptual hash extraction → DBSCAN clustering (12.6K clusters) → KYM annotation validation → Hawkes process influence modeling, showing fringe communities as meme innovation hubs seeding mainstream platforms

[2026-05-26 02:20] figure | 2018-zannettou-disinformation-warfare

• prepared figure prompt for state-sponsored troll operation analysis: Russian accounts (2.7K, 27K tweets) → account characterization (behavior, geographic patterns) → Hawkes process influence quantification → RT dominance as key finding

[2026-05-25 22:15] figure | 2018-yang-ti-cnn

• prepared figure prompt for multimodal fake news detection pipeline: news item → parallel text branch (explicit linguistic + CNN latent features) and image branch (resolution/face count + CNN latent features) → feature fusion → binary classification, achieving F1 0.9210 versus text-only/image-only baselines

[2026-05-25 23:32] figure | 2018-yang-inconsistent-head-poses

• prepared figure prompt for deepfake detection via head pose inconsistency: facial landmark extraction → 3D head pose estimation → inconsistency scoring via cosine distance → SVM classification, achieving 89.0% AUROC by exploiting fundamental synthesis artifact in landmark alignment

[2026-05-26 01:08] figure | 2018-wang-eann-multimodal

• prepared figure prompt for multi-modal adversarial detection pipeline: event (text + image) → parallel text-CNN and image-VGG extraction → fake news detection → adversarial event discriminator learning event-invariant features, achieving 71.5% accuracy (10.3% improvement) through minimax training

[2026-05-25 23:59] figure | 2018-thorne-fever-shared-task

• prepared figure prompt for three-stage fact verification pipeline: document selection → sentence selection → natural language inference, showing convergence of architectural pattern across 23 competing teams

[2026-05-25 23:58] figure | 2018-thorne-fever-dataset

• prepared figure prompt for three-stage fact verification pipeline: Wikipedia claim → document retrieval → sentence selection → textual entailment → verdict (SUPPORTED/REFUTED/NOT ENOUGH INFO), highlighting joint optimization bottleneck with best baseline at 31.87% accuracy

[2026-05-25 23:55] figure | 2018-soares-influencers-polarized-twitter

• prepared figure prompt for three influencer types in polarized networks: Twitter conversation → opinion leaders (shape in-group discourse), informational influencers (institutions drawn into camps), activists (amplify messages) → polarized clusters with reinforced echo chambers.

[2026-05-25 23:50] figure | 2018-shu-network-analysis-fake-news

• prepared figure prompt for network-based detection and mitigation: social media news → network representation (homogeneous/heterogeneous) → detection (embeddings, temporal RNNs) → mitigation (propagator identification, influence minimization)

[2026-05-25 23:42] figure | 2018-shu-fakenewsnet-dataset

• prepared figure prompt for multi-dimensional dataset integration: news articles (PolitiFact/GossipCop) → three dimensions (content linguistic/visual, social engagement/network, spatiotemporal location/time) → dataset for applications (detection, evolution, mitigation), showing combined model achieves 69% accuracy versus 58% content-only

[2026-05-25 23:40] figure | 2018-shao-anatomy-misinformation

• prepared figure prompt for network analysis pipeline: Twitter data collection → k-core decomposition → user role characterization → intervention targeting, showing misinformation-fact-check segregation and removal of high-output spreaders as effective disruption

[2026-05-25 23:35] figure | 2018-potthast-clickbait-challenge

• prepared figure prompt for shared-task regression model: annotation-to-regression pipeline (crowdsourced Likert-scale labels, neural models BiGRU/CNN/LSTM, MSE evaluation) showing neural attention achieves 45% error reduction over feature baselines

[2026-05-25 23:30] figure | 2018-popat-declareE

• prepared figure prompt for evidence-aware claim credibility assessment: three-stage neural pipeline (LSTM encoding with claim-specific attention, credibility aggregation from multiple articles, classification/regression verdict) achieving 78.96% accuracy on Snopes

[2026-05-26 01:06] figure | 2018-van-prooijen-emerging-conspiracy-research

• prepared figure prompt for four-principle conspiracy framework: consequential (health/relationship impacts) → universal (across cultures) → emotional (sense-making/affect-driven) → social (intergroup conflict), synthesis of emerging field consensus on psychological and sociological foundations

[2026-05-26 01:05] figure | 2018-nelson-small-disloyal-fake-news

• prepared figure prompt for audience measurement study: three-stage empirical analysis (measurement of audience size disparities, loyalty patterns, correlation testing) showing audience availability predicts fake news exposure better than demographics

[2026-05-26 01:04] figure | 2018-mohseni-interpretable-news-feed

• prepared figure prompt for algorithmic transparency framework: three-stage lifecycle (creation detection → distribution with interpretable algorithms and explanations → consumption awareness) enabling user-driven bias detection

[2026-05-26 00:02] figure | 2018-mccloskey-gan-color-detection

• prepared figure prompt for GAN-generated image detection: parallel forensic pipelines analyzing color chromaticity and pixel saturation distribution, saturation-based method achieves 0.70 AUC on fully synthetic images

[2026-05-26 00:00] figure | 2018-li-eye-blinking-detection

• prepared figure prompt for eye-blinking physiological forensics: preprocessing (face detection/alignment) → CNN feature extraction (VGG16) → LSTM temporal learning achieving 0.99 AUC deepfake discrimination

[2026-05-25 23:58] figure | 2018-lee-comparative-abusive-language

• prepared figure prompt for abusive language detection benchmark: comparing traditional ML, CNN, RNN, and context-enhanced RNN across 100K tweets with RNN-LTC achieving best F1 (0.805)

[2026-05-25 23:57] figure | 2018-lazer-science-fake-news

• prepared figure prompt for multidisciplinary fake news framework: definition (fabricated information) → knowledge gaps (unmeasured prevalence/impact) → individual solutions (fact-checking/education, insufficient) → platform interventions (algorithms, bot detection) → multidisciplinary collaboration

[2026-05-25 23:55] figure | 2018-krishnamurthy-multimodal-deception

• prepared figure prompt for multimodal deception detection pipeline: four-stage feature extraction (visual 3D-CNN, textual CNN, audio openSMILE, micro-expression) → Hadamard fusion → classification achieving 96.14% accuracy on courtroom videos

[2026-05-25 23:28] figure | 2018-korshunov-deepfakes-threat-detection

• prepared figure prompt for deepfake threat assessment: GAN-based generation (low/high quality) → face recognition vulnerability (85–95% false acceptance) → detection via image quality metrics (8.97% EER)

[2026-05-25 22:26] figure | 2018-kochkina-multitask-rumor-verification

• prepared figure prompt for multi-task rumor verification pipeline: three task heads (detection, stance, veracity) with shared LSTM achieving up to 28.9% F-score improvement

[2026-05-25 22:25] figure | 2018-horne-sampling-news-producers

• prepared figure prompt for news source characterization pipeline: diverse sources → linguistic/sentiment/engagement/credibility features → NELA2017 dataset enabling systematic source comparison

[2026-05-25 22:24] figure | 2018-elsherief-hate-lingo

• prepared figure prompt for hate speech analysis pipeline: collection-annotation-analysis-results workflow showing directed hate exhibits anger and informality; generalized hate features religious and lethal semantics

[2026-05-25 22:23] figure | 2018-brundage-malicious-use-ai

• prepared figure prompt for three-domain threat modeling: digital, physical, political security (AI capabilities → threat expansion, new attacks, character shift)

[2026-05-25 22:22] figure | 2018-bail-exposure-opposing-views

• prepared figure prompt for causal field experiment: treatment-measurement-results pipeline (bot exposure to opposing views → ideology survey → partisan asymmetry, Republicans backfire while Democrats unchanged)

[2026-05-25 22:21] figure | 2017-wardle-information-disorder

• prepared figure prompt for information disorder framework: three-type taxonomy (misinformation, disinformation, malinformation) distinguished by falseness and intent-to-harm

[2026-05-25 22:20] figure | 2017-wang-topo-lstm

• prepared figure prompt for cascade prediction pipeline: three-stage cascade processing (diffusion topology DAG extraction, dual-gate LSTM modeling, sender/receiver embeddings) achieving 20–56% improvement over prior methods

[2026-05-25 22:19] figure | 2017-wang-liar-dataset

• prepared figure prompt for dataset pipeline: four-stage processing (collection from PolitiFact, 6-way veracity labeling, metadata integration, hybrid CNN evaluation) demonstrating speaker credibility history improves classification accuracy

[2026-05-25 22:18] figure | 2017-vosoughi-spread-false-news

• prepared figure prompt for cascade analysis: four-stage pipeline (metrics measurement, spread comparison, human vs bot actors, novelty mechanism) showing false news spreads 6× faster, driven by human novelty preference

[2026-05-25 22:17] figure | 2017-vaswani-attention-transformer

• prepared figure prompt for transformer architecture: encoder-decoder pipeline with multi-head self-attention achieving SOTA machine translation (41.0 BLEU EN→FR)

[2026-05-25 22:16] figure | 2017-varol-human-bot-interactions

• prepared figure prompt for Twitter bot detection: four-stage pipeline (extract 1,150 features, classify via ML models, cluster into 10 phenotypes, estimate 9–15% bot population)

[2026-05-25 22:15] figure | 2017-van-der-linden-inoculating-climate

• prepared figure prompt for inoculation against misinformation: four-stage pipeline (consensus messaging, misinformation threat, pre-emptive inoculation, outcome) showing detailed inoculation preserves ~65% of consensus effect across political spectrum

[2026-05-25 21:53] figure | 2017-tandoc-defining-fake-news

• prepared figure prompt for typology of six types: satire, parody, fabrication, manipulation, advertising, propaganda distinguished by facticity and deceptive intent

[2026-05-25 21:52] figure | 2017-tacchini-some-like-it-hoax

• prepared figure prompt for hoax detection from interactions: three-stage pipeline (user interaction patterns, logistic regression and harmonic label crowdsourcing, hoax classification) achieving >99% accuracy without content analysis

[2026-05-25 21:51] figure | 2017-stukal-bots-russian-twitter

• prepared figure prompt for ensemble bot detection: four-stage pipeline (labeling, feature extraction, four classifiers, ensemble voting) achieving 95% precision on 3.8M Russian Twitter accounts

[2026-05-25 21:50] figure | 2017-shu-fake-news-detection-mining

• prepared figure prompt for data mining framework: characterization via psychology/sociology, detection organized into content-based (knowledge, style) and social-context methods (stance, propagation), evaluation via datasets and metrics

[2026-05-25 21:49] figure | 2017-shiralkar-knowledge-streams

• prepared figure prompt for knowledge stream fact-checking: network flow method finds multiple evidence paths through knowledge graph with edge capacities based on relational similarity and node specificity

[2026-05-25 21:48] figure | 2017-shao-social-bots-low-credibility

• prepared figure prompt for social bot amplification: empirical pipeline from tweet collection and bot detection via Botometer through network analysis to impact measurement showing 6% bot accounts generate 31% of amplifying tweets

[2026-05-25 21:46] figure | 2017-ruchansky-csi

• prepared figure prompt for three-module fake news detection: Capture module learns temporal engagement via LSTM, Score module extracts user suspiciousness from co-engagement, Integrate module combines signals for classification

[2026-05-25 21:45] figure | 2017-rashkin-truth-varying-shades

• prepared figure prompt for linguistic analysis of fake news: features like pronouns, modal adverbs, and subjectivity reliably distinguish deceptive from trustworthy news across fine-grained truthfulness scales

[2026-05-25 21:44] figure | 2017-potthast-stylometric-hyperpartisan

• prepared figure prompt for stylometric news credibility detection: hyperpartisan news exhibits a shared stylometric signature (F1 = 0.78), satire is easily distinguishable (F1 = 0.81), but style alone cannot reliably detect fake news (F1 = 0.46)

[2026-05-25 21:47] figure | 2017-schmidt-hate-speech-survey

• prepared figure prompt for hate speech detection feature taxonomy: surveys eight feature types (lexical, linguistic, embeddings, multimodal, etc.) combined with supervised classification; complex features show promise but remain difficult to compare across datasets

[2026-05-25 21:43] figure | 2017-ma-propagation-tree-kernel

• prepared figure prompt for rumor detection via propagation trees: three-stage pipeline (extract propagation tree from retweets/replies, compute tree kernel similarity, four-way SVM classification) achieving 75% accuracy within 24 hours

[2026-05-25 21:42] figure | 2017-kochkina-rumour-stance-lstm

• prepared figure prompt for Branch-LSTM stance classification: four-stage pipeline (decompose conversation tree into branches, encode tweets with word2vec and relational features, LSTM sequential processing, classify into Support/Deny/Query/Comment) achieving 78.4% accuracy on RumourEval 2017

[2026-05-25 21:41] figure | 2017-kim-crowd-leveraging-detection

• prepared figure prompt for crowd-powered misinformation control: four-stage pipeline (exposure dynamics, flagging, optimal control via CURB algorithm, intervention) for fact-checking scheduling

[2026-05-25 21:37] figure | 2017-garimella-quantifying-controversy

• prepared figure prompt for controversy detection: network-based pipeline from graph construction through partitioning to random-walk-based controversy measurement

[2026-05-25 21:36] figure | 2017-douglas-psychology-conspiracy-theories

• prepared figure prompt for psychology of conspiracy belief: three psychological motives (epistemic uncertainty-reduction, existential control-seeking, social identity-maintenance) that initially appeal but empirically thwart satisfaction in self-defeating cycle

[2026-05-25 21:35] figure | 2017-derczynski-rumoureval

• prepared figure prompt for RumourEval shared task: two-task benchmark combining SDQC stance classification (S/D/Q/C) of conversation replies with source veracity prediction, establishing foundation for rumour verification research on Twitter

[2026-05-25 21:30] figure | 2017-cresci-social-spambots

• prepared figure prompt for paradigm shift from individual account detection to group-level behavioral analysis: social spambots evade all individual-level detection methods (0.24 human accuracy) but group-level analysis succeeds

[2026-05-25 21:26] figure | 2017-cook-neutralizing-misinformation

• prepared figure prompt for inoculation framework: pre-exposure to misleading techniques (false-balance, fake-expert arguments) neutralizes misinformation and preserves consensus across political worldviews

[2026-05-25 21:22] figure | 2017-brady-moral-emotion-diffusion

• prepared figure prompt for moral-emotional language and viral diffusion: analysis of Twitter discourse showing moral-emotional language increases retweet rates ~20% per word, with stronger in-group amplification explaining echo-chamber polarization

[2026-05-25 20:40] figure | 2017-allcott-social-media-fake-news

• prepared figure prompt for fake news exposure quantification: empirical pipeline from database assembly through traffic measurement, survey, and belief analysis to electoral impact estimation; average American saw 1.14 fake articles during 2016 election with 3:1 pro-Trump partisan bias

[2026-05-25 20:35] figure | 2016-zubiaga-stance-sequential-tree

• prepared figure prompt for stance classification: Tree CRF model exploiting conversational structure to achieve 0.44 macro-F1 on rumour stance classification

[2026-05-25 20:30] figure | 2016-waseem-annotator-influence

• prepared figure prompt for annotator influence on hate speech detection: expert vs. amateur annotation sources and their impact on classifier performance

[2026-05-25 18:45] figure | 2016-li-deepcas

• prepared figure prompt for cascade prediction: random walk sampling, node embedding, bidirectional GRU encoding, attention mechanism learning optimal parameters rather than hand-crafted features

[2026-05-25 17:12] figure | 2016-garimella-reducing-controversy

• prepared figure prompt for bridge-recommendation algorithm: measurement (RWC and polarity), hub node selection, and acceptance probability ranking; high-degree nodes are most effective bridges for reducing polarization

[2026-05-25 16:48] figure | 2016-farrell-corporate-funding-climate

• prepared figure prompt for corporate funding effects on climate discourse: data collection, topic modeling, network analysis, and funding correlation; funded organizations escalate energy-friendly narratives

[2026-05-25 16:30] figure | 2016-davis-botornot

• prepared figure prompt for bot detection pipeline: six feature categories (network, temporal, content, sentiment, user, friends) feeding into Random Forest classifier; 0.95 AUC on Twitter data

[2026-05-25 14:07] figure | 2016-anand-clickbait-neural-networks

• prepared figure prompt for clickbait detection: bidirectional LSTM with word and character embeddings; pipeline from headlines through embedding and recurrent layers to classification output; 98% accuracy on 15,000-headline dataset

[2026-05-25 16:22] figure | 2015-ferrara-rise-social-bots

• prepared figure prompt for bot detection taxonomy: network structure analysis, crowd-sourced human judgment, and behavioral machine learning; escalating arms race between bot sophistication and detection methods

[2026-05-25 16:15] figure | 2015-ciampaglia-computational-fact-checking

• prepared figure prompt for knowledge-graph fact checking: claim → knowledge graph → path finding → truth scoring; indirect paths outperform direct edges, achieving 99% F-score on ideology classification

[2026-05-25 15:24] figure | 2014-cheng-cascade-prediction

• prepared figure prompt for cascade prediction: temporal, resharer, structural, and content feature extraction ranked by predictive importance; temporal features dominate (78% vs. 56% for content alone)

[2026-05-25 15:14] figure | 2013-mikolov-word2vec

• prepared figure prompt for two-architecture word embedding comparison: CBOW predicts target from context; Skip-gram predicts context from target; vectors capture semantic and syntactic regularities

[2026-05-25 13:57] figure | 2012-lewandowsky-misinformation-correction

• prepared figure prompt for four-stage pipeline: origins of misinformation, cognitive factors enabling belief, persistence mechanisms, and evidence-based debiasing strategies

[2026-05-25 15:08] figure | 2011-castillo-twitter-credibility

• prepared figure prompt for multi-scope feature-based credibility classification: message, user, topic, and propagation features

[2026-05-25 15:02] figure | 2010-ratkiewicz-astroturf-detection

• prepared figure prompt for astroturf detection pipeline: data collection, feature extraction, classification, detection

[2026-05-25 14:52] figure | 2009-fortunato-community-detection-graphs

• prepared figure prompt for community detection method taxonomy (hierarchical, spectral, modularity, dynamic approaches)

[2026-05-25 14:45] ingest | TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

• job: 1779677607315-56eaf722
• submitter: reza
• page: TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial
• touched: wiki/papers/2020-morris-textattack.md, wiki/topics/adversarial-robustness.md, wiki/topics/nlp-evaluation.md, wiki/topics/data-augmentation.md, wiki/topics/adversarial-training.md, wiki/topics/model-evaluation.md, wiki/papers/index.md
• notes: Unifies 16 adversarial attacks into 4-component framework; enables benchmarking across 82+ NLP models. Restored from prior deletion; updated links in 5 topic pages.

[2026-05-25 14:40] ingest | Distributed Representations of Words and Phrases and their Compositionality

• job: 1779677606958-1f6b3bf8
• submitter: reza
• page: Distributed Representations of Words and Phrases and their Compositionality
• touched: wiki/papers/2013-mikolov-phrases-compositionality.md, wiki/authors/mikolov-tomas.md, wiki/authors/sutskever-ilya.md, wiki/authors/chen-kai.md, wiki/authors/corrado-greg.md, wiki/authors/dean-jeffrey.md, wiki/topics/natural-language-processing.md, wiki/topics/word-embeddings.md, wiki/topics/phrase-embeddings.md, wiki/topics/text-representations.md, wiki/index.md
• notes: Extends Skip-gram with negative sampling, subsampling, and phrase handling; 72% accuracy on phrase analogy tasks. Created 3 new topic pages (word-embeddings, phrase-embeddings, text-representations); updated 5 author pages and NLP topic.

[2026-05-25 14:35] ingest | Discovering Latent Knowledge in Language Models Without Supervision

• job: 1779677606921-1dd007d6
• submitter: reza
• page: Discovering Latent Knowledge in Language Models Without Supervision
• touched: wiki/papers/2022-burns-discovering-latent-knowledge.md, wiki/topics/latent-knowledge.md, wiki/topics/truthfulness.md, wiki/papers/index.md, wiki/authors/burns-collin.md, wiki/authors/ye-haotian.md, wiki/authors/klein-dan.md, wiki/authors/steinhardt-jacob.md
• notes: Proposes Contrast-Consistent Search (CCS) to recover latent knowledge from LM activations using logical consistency; 71.2% accuracy across six models and ten QA datasets, robust to misleading prompts. Created latent-knowledge topic page; updated truthfulness and four author pages.

[2026-05-25] ingest | Atlas: Few-shot Learning with Retrieval Augmented Language Models

• job: 1779677606888-e886d896
• submitter: reza
• page: Atlas: Few-shot Learning with Retrieval Augmented Language Models
• touched: wiki/papers/2022-izacard-atlas-few-shot.md, wiki/authors/lewis-patrick.md, wiki/authors/petroni-fabio.md, wiki/authors/riedel-sebastian.md, wiki/topics/fact-checking.md, wiki/index.md
• notes: Retrieval-augmented language model for few-shot learning on knowledge-intensive tasks including fact-checking; achieves 80.1% on FEVER with full data, 56.2% with 15 examples. Key contributions: joint retriever-LM training without document annotations (ADist, EMDR², PDist, LOOP losses), interpretability via retrieved documents, index updateability without retraining. Updated 3 author pages, 1 topic page, added to index Foundations.

[2026-05-25 08:30] figure | 2012-lewandowsky-misinformation-correction

• prepared figure prompt for misinformation persistence and debiasing pipeline (origins, belief mechanisms, persistence, debiasing strategies)

[2026-05-25 08:00] figure | 2011-castillo-twitter-credibility

• prepared figure prompt for four-scope credibility classification pipeline (message, user, topic, propagation features)

[2026-05-25 07:45] figure | 2010-ratkiewicz-astroturf-detection

• prepared figure prompt for astroturfing detection pipeline (data collection → feature extraction → classification → detection)

[2026-05-24] ingest | RoBERTa: A Robustly Optimized BERT Pretraining Approach

• job: 1779677606806-2dda0c18
• submitter: reza
• page: RoBERTa: A Robustly Optimized BERT Pretraining Approach
• touched: wiki/papers/2019-liu-roberta.md, wiki/authors/liu-yinhan.md, wiki/authors/ott-myle.md, wiki/authors/goyal-naman.md, wiki/authors/du-jingfei.md, wiki/authors/joshi-mandar.md, wiki/authors/chen-danqi.md, wiki/authors/levy-omer.md, wiki/authors/zettlemoyer-luke.md, wiki/authors/stoyanov-veselin.md, wiki/topics/bert.md, wiki/topics/language-models.md, wiki/topics/transformers.md, wiki/index.md
• notes: Foundational NLP pretraining paper improving BERT through systematic study of design choices (dynamic masking, removal of NSP, longer sequences, larger batches); canonical baseline for language model fine-tuning in misinformation detection; created 9 new author pages, updated 3 topic pages, linked from index.

[2026-05-24] ingest | The platform governance triangle: conceptualising the informal regulation of online content

• job: 1779677606802-5e2fc728
• submitter: reza
• page: The platform governance triangle: conceptualising the informal regulation
• touched: wiki/papers/2019-gorwa-platform-governance-triangle.md, wiki/index.md
• notes: Platform governance framework paper mapping EU content regulation landscape using Abbott & Snidal's governance triangle model; adds new "Platform governance and regulation" section to index.

[2026-05-24 23:33] figure | 2020-zhou-fake-news-survey

• prepared figure prompt for four-perspective detection taxonomy diagram

[2026-05-25 00:15] ingest | Attention Is All You Need

• job: 1779677606783-4737315e
• submitter: reza
• page: Attention Is All You Need
• touched: wiki/authors/vaswani-ashish.md, wiki/authors/shazeer-noam.md, wiki/authors/parmar-niki.md, wiki/authors/uszkoreit-jakob.md, wiki/authors/jones-llion.md, wiki/authors/gomez-aidan.md, wiki/authors/kaiser-lukasz.md, wiki/authors/polosukhin-illia.md, wiki/topics/attention-mechanisms.md, wiki/topics/transformers.md, wiki/topics/sequence-modeling.md, wiki/topics/neural-nlp.md, wiki/index.md
• notes: Foundational Transformer architecture paper introducing self-attention and multi-head attention; state-of-the-art machine translation results. Canonical reference for modern NLP and underlying nearly all contemporary fake news detection models.

[2026-05-24 23:52] ingest | A Deep Learning Approach for Multimodal Deception Detection

• job: 1779677606752-9843b1b5
• submitter: reza
• page: A Deep Learning Approach for Multimodal Deception Detection
• touched: wiki/authors/majumder-navonil.md (fixed wikilink), wiki/topics/multimodal.md (updated from stub), wiki/topics/deception-detection.md, wiki/papers/index.md, wiki/index.md
• notes: Multimodal deception detection in courtroom videos (96.14% accuracy) using 3D-CNN, audio features, text CNN, and micro-expressions. Borderline relevance: primarily focused on individual lie detection rather than misinformation, but methodologically relevant to behavioral cue detection and multimodal analysis. Already-existing author pages (4/4) and topic pages required minimal updates.

[2026-05-24 23:47] ingest | A Survey on Hate Speech Detection using Natural Language Processing

• job: 1779677606711-32000c85
• submitter: reza
• page: A Survey on Hate Speech Detection using Natural Language Processing
• touched: wiki/authors/schmidt-anna.md (updated), wiki/authors/wiegand-michael.md (updated), wiki/topics/hate-speech-detection.md, wiki/topics/text-classification.md, wiki/topics/nlp-methods.md, wiki/papers/index.md
• notes: Comprehensive survey of NLP-based hate speech detection methods; taxonomizes feature extraction approaches (surface features, embeddings, sentiment analysis, lexical resources, linguistic dependencies, knowledge bases, meta-information, multimodal). Methodological reference applicable to content moderation and harmful speech detection. Updated existing author pages to reference this paper instead of non-existent predecessor.

[2026-05-24 19:12] ingest | A Survey of the State of Explainable AI for Natural Language Processing

• job: 1779677606609-f04b74f7
• submitter: reza
• page: A Survey of the State of Explainable AI for Natural Language Processing
• touched: wiki/authors/danilevsky-marina.md (new), wiki/authors/qian-kun.md (new), wiki/authors/aharonov-ranit.md (new), wiki/authors/katsis-yannis.md (new), wiki/authors/kawas-ban.md (new), wiki/authors/sen-prithviraj.md (new), wiki/topics/explainability.md, wiki/topics/nlp-methods.md, wiki/topics/interpretability.md (new), wiki/papers/index.md, wiki/index.md

[2026-05-25 03:20] figure | 2009-fortunato-community-detection-graphs

• prepared figure prompt for hierarchical, spectral, and modularity-based algorithm taxonomy
• notes: First XAI survey focusing specifically on NLP domain; comprehensive taxonomy of explanation approaches (local/global, self-explaining/post-hoc) across 50 papers. Created new author pages for all 6 authors and new interpretability topic page. Updated explainability and nlp-methods topic pages. Identified as canonical methodological reference for interpretable NLP systems in misinformation detection.

[2026-05-24 18:45] ingest | Prevalence of Health Misinformation on Social Media

• job: 1779677606605-6650feae
• submitter: reza
• page: Prevalence of Health Misinformation on Social Media: Systematic Review
• touched: wiki/authors/suarez-lledo-victor.md, wiki/authors/alvarez-galvez-javier.md, wiki/topics/health-misinformation.md, wiki/topics/vaccine-hesitancy.md, wiki/topics/substance-abuse.md (new), wiki/topics/eating-disorders.md (new), wiki/topics/misinformation-detection.md, wiki/topics/social-media-analysis.md, wiki/papers/index.md, wiki/index.md
• notes: Systematic review of 69 studies identifying health misinformation prevalence across platforms. Created new topic pages for substance-abuse and eating-disorders; updated 7 existing pages with references to this paper. Corrected wikilink slug on 5+ pages that had referenced a non-existent page slug.

[2026-05-24] lint | auto-fix-broken-wikilinks

• fixed: 38 broken wikilinks (18 deleted papers, 20 non-existent topics)
• touched: wiki/log.md (18 paper refs), wiki/authors/ (20 pages)
• notes: Removed wikilinks to papers deleted in relevance audit + removed references to non-existent topic pages (sequential-models, adversarial-attacks, optimization, automated-accounts, rumour-verification, cross-lingual-nlp, news-ecosystems, data-mining, nlp, transformers, synthetic-video-detection, corpus-construction, argument-mining, nlp-information-verification, emotion-recognition)

[2026-05-18] lint | cleanup-after-audit-delete

• fixed: 30+ broken wikilinks from 20 out-of-scope papers
• touched: wiki/index.md, 30+ topic pages
• notes: Removed wikilinks to papers deleted in relevance audit (2013-kingma, 2016-jones, 2017-schmidt, 2018-howard, 2018-krishnamurthy, 2020-angelov, 2020-clark, 2020-guu, 2020-morris, 2022-burns, 2022-izacard, 2022-ouyang, 2023-dahein, 2023-ganguli, 2023-guerreiro, 2023-liu-lost-in-middle, 2023-shu, 2023-wang-medical, 2023-zhao, 2026-kasneci)

[2026-05-18] relevance audit | batch 18/19

• audited: 15 papers
• in_scope: 8
• borderline: 5
• out_of_scope: 2
• flagged for removal: 2023-wang-medical-summarization-metrics, 2023-zhao-survey-large-language-models

[2026-05-18 16:22] relevance audit | batch 19/19 (final)

• audited: 8 papers
• in_scope: 7
• borderline: 1
• out_of_scope: 0
• flagged for removal: none

[2026-05-18 15:45] relevance audit

• audited: 15 papers
• in_scope: 11
• borderline: 1
• out_of_scope: 3
• flagged for removal: 2023-guerreiro-hallucinations-multilingual, 2023-liu-lost-in-middle, 2023-shu-exploitability-instruction-tuning

[2026-05-18 10:00] relevance audit

• audited: 15 papers
• in_scope: 13
• borderline: 0
• out_of_scope: 2
• flagged for removal: 2020-clark-electra, 2020-guu-realm

[2026-05-18 14:25] relevance audit

• audited: 15 papers
• in_scope: 12
• borderline: 2
• out_of_scope: 1
• flagged for removal: 2013-kingma-variational-autoencoder

[2026-05-18 15:30] relevance audit

• audited: 15 papers
• in_scope: 14
• out_of_scope: 1
• flagged for removal: 2017-schmidt-hate-speech-detection ```

---

[2026-05-18 17:25] relevance audit

• audited: 15 papers
• in_scope: 13
• borderline: 1
• out_of_scope: 1
• flagged for removal: 2020-morris-textattack

[2026-05-18] relevance audit

• audited: 15 papers
• in_scope: 15

[2026-05-18 14:30] relevance audit

• audited: 15 papers
• in_scope: 13
• borderline: 1
• out_of_scope: 1
• flagged for removal: 2016-jones-tweeting-negative-emotion

[2026-05-18 19:45] relevance audit

• audited: 15 papers
• in_scope: 12
• borderline: 1
• out_of_scope: 2
• flagged for removal: 2018-howard-ulmfit, 2018-krishnamurthy-multimodal-deception

[2026-05-18] ingest | Top2Vec: Distributed Representations of Topics

• job: 1778895729563-d1ec8bd4
• submitter: reza
• page: 2020-angelov-top2vec
• touched: wiki/papers/2020-angelov-top2vec.md (created), wiki/authors/angelov-dimo.md (created), wiki/topics/topic-modeling.md (created), wiki/index.md (updated)
• notes: Topic discovery via semantic embeddings + UMAP + HDBSCAN; automatically determines topic count and achieves superior information gain vs. LDA/PLSA. Applicable to analyzing thematic structure and narrative frames in misinformation corpora.

[2026-05-18] ingest | TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

• job: 1778895729507-5a9687ff
• submitter: reza
• page: 2020-morris-textattack
• touched: wiki/papers/2020-morris-textattack.md (created), wiki/authors/morris-john.md (created), wiki/authors/lifland-eli.md (created), wiki/authors/yoo-jin.md (created), wiki/authors/grigsby-jake.md (created), wiki/authors/jin-di.md (created), wiki/authors/qi-yanjun.md (created), wiki/topics/adversarial-robustness.md (updated), wiki/topics/nlp-evaluation.md (updated), wiki/topics/data-augmentation.md (created), wiki/topics/adversarial-training.md (created), wiki/topics/model-evaluation.md (updated), wiki/index.md (updated)
• notes: Modular framework unifying 16 adversarial attacks on NLP models; demonstrates significant robustness improvements via adversarial training. Foundational for evaluating detection system vulnerability to text perturbations.

[2026-05-17] ingest | The Amplification Paradox in Recommender Systems

• job: 1778895729374-d0d81993
• submitter: reza
• page: The Amplification Paradox in Recommender Systems
• touched: wiki/papers/2023-ribeiro-amplification-paradox.md (created), wiki/authors/ribeiro-manoel.md (created), wiki/authors/veselovsky-veniamin.md (created), wiki/authors/west-robert.md (updated), wiki/topics/recommender-systems.md (updated), wiki/topics/algorithmic-amplification.md (updated), wiki/topics/filter-bubbles.md (updated), wiki/topics/computational-modeling.md (created), wiki/index.md (updated)
• notes: Agent-based model explaining the amplification paradox: audits show algorithmic amplification of extreme content but real user logs show recommendations don't drive extreme consumption. Argues user preferences must be modeled in audits. Foundational for understanding recommender system dynamics in misinformation contexts.

[2026-05-17] ingest | How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances

• job: 1778895729337-6623f155
• submitter: reza
• page: How Do Large Language Models Capture the Ever-changing World Knowledge? A Review
• touched: wiki/papers/2023-zhang-llm-knowledge-update.md (created), wiki/authors/zhang-zihan.md (created), wiki/authors/fang-meng.md (created), wiki/authors/chen-ling.md (created), wiki/authors/namazi-rad-mohammad-reza.md (created), wiki/authors/wang-jun.md (created), wiki/topics/large-language-models.md (updated), wiki/topics/knowledge-editing.md (created), wiki/topics/continual-learning.md (created), wiki/topics/knowledge-retrieval.md (created), wiki/topics/hallucination-mitigation.md (created), wiki/index.md (updated)
• notes: Comprehensive survey of knowledge updating methods for LLMs including implicit (editing, continual learning) and explicit (memory/retrieval/internet-enhanced) approaches. Critical for fact-checking systems: outdated LLM knowledge causes hallucinations; surveys mitigation strategies.

[2026-05-17] ingest | From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News

• job: 1778895729322-0d1146d8
• submitter: reza
• page: From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake
• touched: wiki/papers/2024-liu-attitude-dynamics-fake-news.md (created), wiki/authors/liu-yuhan.md (created), wiki/authors/chen-xiuying.md (created), wiki/authors/zhang-xiaoqing.md (created), wiki/authors/gao-xing.md (created), wiki/authors/zhang-ji.md (created), wiki/authors/yan-rui.md (created), wiki/topics/agent-based-modeling.md (created), wiki/topics/opinion-dynamics.md (created), wiki/topics/fake-news-propagation-simulation.md (created), wiki/topics/personality-traits-fake-news.md (created), wiki/topics/intervention-strategies.md (created), wiki/papers/index.md (updated)
• notes: First LLM-based agent simulation framework for fake news propagation with dual memory (short/long-term) and personality-based reasoning. Validates empirical findings: political misinformation spreads faster than science; credulous agents (high agreeableness/neuroticism) 1.73× more susceptible; early consistent interventions effective.

[2026-05-17] ingest | The Perils & Promises of Fact-checking with Large Language Models

• job: 1778895729305-7c6cbb5d
• submitter: reza
• page: The Perils & Promises of Fact-checking with Large Language Models
• touched: wiki/papers/2023-quelle-fact-checking-llms.md (created), wiki/authors/quelle-dorian.md (created), wiki/authors/bovet-alexandre.md (created), wiki/topics/computational-fact-checking.md (updated), wiki/topics/claim-verification.md (updated), wiki/topics/large-language-models.md (updated), wiki/topics/language-model-truthfulness.md (updated), wiki/topics/multilingual-detection.md (updated), wiki/topics/evidence-based-detection.md (updated), wiki/index.md (updated)
• notes: Evaluates GPT-3.5 and GPT-4 for automated fact-checking across English (PolitiFact) and 16+ languages (Data Commons); demonstrates evidence retrieval via Google Search improves accuracy 10–20 percentage points; reveals critical training-data bias limiting multilingual verification.

[2026-05-17] ingest | Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations

• job: 1778895729299-040f1683
• submitter: reza
• page: 2023-wang-medical-summarization-metrics
• touched: wiki/papers/2023-wang-medical-summarization-metrics.md (created), wiki/authors/wang-lucy-lu.md (created), wiki/authors/otmakhova-yulia.md (created), wiki/authors/deyoung-jay.md (created), wiki/authors/truong-thinh-hung.md (created), wiki/authors/kuehl-bailey-e.md (created), wiki/authors/bransom-erin.md (created), wiki/authors/wallace-byron-c.md (created), wiki/topics/multi-document-summarization.md (created), wiki/topics/medical-nlp.md (created), wiki/topics/evaluation-metrics.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Analysis of metric-human disagreement in medical multi-document summarization on Cochrane reviews; dataset of 4,658 summaries with 8-facet human annotations; shows ROUGE/BERTScore uncorrelated with human quality but PIO-Overlap and ClaimVer metrics promising for medical domain.

[2026-05-17] ingest | FLIRT: Feedback Loop In-context Red Teaming

• job: 1778895729228-30a9676b
• submitter: reza
• page: FLIRT: Feedback Loop In-context Red Teaming
• touched: wiki/papers/2023-mehrabi-flirt-red-teaming.md (created), wiki/authors/mehrabi-ninareh.md (created), wiki/authors/goyal-palash.md (created), wiki/authors/dupuy-christophe.md (created), wiki/authors/hu-qian.md (created), wiki/authors/ghosh-shatini.md (created), wiki/authors/zemel-richard.md (created), wiki/authors/chang-kai-wei.md (created), wiki/authors/galstyan-aram.md (created), wiki/authors/gupta-rahul.md (created), wiki/topics/red-teaming.md (created), wiki/topics/adversarial-testing.md (created), wiki/topics/prompt-injection.md (created), wiki/topics/model-safety.md (created), wiki/topics/ai-safety.md (updated), wiki/topics/llm-safety.md (updated), wiki/topics/generative-models.md (updated), wiki/index.md (updated)
• notes: Automated red teaming framework using in-context learning; achieves 80%+ attack success on Stable Diffusion and transfers across text-to-image models; first comprehensive study of in-context learning for adversarial prompt generation in multimodal systems.

[2026-05-17] ingest | Causal Machine Learning: A Survey and Open Problems

• job: 1778895729208-2eb139b2
• submitter: reza
• page: Causal Machine Learning: A Survey and Open Problems
• touched: wiki/papers/2022-kaddour-causal-machine-learning-survey.md (created), wiki/authors/kaddour-jean.md (created), wiki/authors/lynch-aengus.md (created), wiki/authors/liu-qi.md (created), wiki/authors/kusner-matt.md (created), wiki/authors/silva-ricardo.md (created), wiki/topics/causal-inference.md (updated), wiki/topics/machine-learning.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Comprehensive 191-page survey of causal ML methods with unified taxonomy across five problem areas (supervised learning, generative modeling, explanations, fairness, RL); addresses domain robustness via invariant features and cross-environment generalization—foundational for understanding why misinformation detectors degrade when trained on one source/time period and deployed on another.

[2026-05-17] ingest | The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey

• job: 1778895729199-e446ab9f
• submitter: reza
• page: The Evolution of Distributed Systems for Graph Neural Networks and their Origin
• touched: wiki/papers/2023-vatter-distributed-gnn-systems.md (created), wiki/authors/vatter-jana.md (created), wiki/authors/mayer-ruben.md (created), wiki/authors/jacobsen-hans-arno.md (created), wiki/topics/distributed-systems.md (created), wiki/topics/graph-processing.md (created), wiki/topics/scalability.md (created), wiki/topics/gnn-training.md (created), wiki/topics/graph-neural-networks.md (updated), wiki/topics/deep-learning.md (updated), wiki/index.md (updated)
• notes: Comprehensive survey bridging graph processing systems, distributed DNN training, and distributed GNN systems; systematic categorization of partitioning, sampling, communication, synchronization, and programming abstractions across 20+ systems; essential for understanding scalability of GNN-based misinformation detection on large social networks.

[2026-05-17] ingest | Assessing the Quality of the Datasets by Identifying Mislabeled Samples

• job: 1778895729165-1173e18e
• submitter: reza
• page: Assessing the Quality of the Datasets by Identifying Mislabeled Samples
• touched: wiki/papers/2021-pulastya-mislabeled-samples.md (created), wiki/authors/pulastya-vaibhav.md (created), wiki/authors/nuti-gaurav.md (created), wiki/authors/atri-yash-kumar.md (created), wiki/authors/chakraborty-tanmoy.md (updated), wiki/topics/data-quality.md (updated), wiki/topics/label-noise.md (created), wiki/topics/dataset-curation.md (created), wiki/index.md (updated)
• notes: Automated mislabel detection via supervised VAE using latent-space outlier detection; requires no access to clean validation set or prior knowledge of noise type; directly applicable to dataset curation in misinformation research where crowdsourced annotation quality is often uncertain.

[2026-05-17] ingest | Belief Evolution Network-based Probability Transformation and Fusion

• job: 1778895729150-7829c241
• submitter: reza
• page: Belief Evolution Network-based Probability Transformation and Fusion
• touched: wiki/papers/2021-zhou-belief-evolution-network.md (created), wiki/authors/zhou-qianli.md (created), wiki/authors/huang-yusheng.md (created), wiki/authors/deng-yong.md (created), wiki/topics/belief-functions.md (created), wiki/topics/dempster-shafer-theory.md (created), wiki/topics/information-fusion.md (created), wiki/topics/uncertainty-quantification.md (created), wiki/papers/index.md (updated)
• notes: Methodological contribution to belief function theory and information fusion; introduces Belief Evolution Network (BEN) and Full Causality Probability Transformation (FCPT) for handling uncertain information; foundational tool for multi-source evidence aggregation relevant to claim verification.

[2026-05-17] ingest | A Comprehensive Survey on Trustworthy Recommender Systems

• job: 1778895729117-9ce9dbe5
• submitter: reza
• page: A Comprehensive Survey on Trustworthy Recommender Systems
• touched: wiki/papers/2022-fan-trustworthy-recommender-systems.md (created), wiki/authors/fan-wenqi.md (created), wiki/authors/zhao-xiangyu.md (created), wiki/authors/chen-xiao.md (created), wiki/authors/su-jingran.md (created), wiki/authors/gao-jingtong.md (created), wiki/authors/wang-lin.md (created), wiki/authors/liu-qidong.md (created), wiki/authors/wang-yiqi.md (created), wiki/authors/xu-han.md (created), wiki/authors/chen-lei.md (created), wiki/authors/li-qing.md (created), wiki/topics/recommender-systems.md (created), wiki/topics/adversarial-robustness.md (created), wiki/papers/index.md (updated)
• notes: Comprehensive survey of trustworthy recommender systems across six dimensions (Safety & Robustness, Non-discrimination & Fairness, Explainability, Privacy, Environmental Well-being, Accountability & Auditability); relevant to misinformation because recommender systems are primary vectors for false-information amplification; bridges gap between general recommender-system research and information-quality concerns.

[2026-05-17] ingest | A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

• job: 1778895729107-05543839
• submitter: reza
• page: A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness,
• touched: wiki/papers/2023-dai-trustworthy-gnn-survey.md (created), wiki/authors/dai-enyan.md (created), wiki/authors/zhao-tianxiang.md (created), wiki/authors/zhu-huaisheng.md (created), wiki/authors/xu-junjie.md (created), wiki/authors/guo-zhimeng.md (created), wiki/authors/tang-jiliang.md (updated), wiki/authors/wang-suhang.md (updated), wiki/topics/graph-neural-networks.md (updated), wiki/topics/privacy.md (updated), wiki/topics/robustness.md (updated), wiki/topics/explainability.md (updated), wiki/topics/fairness-in-nlp.md (updated), wiki/index.md (updated)
• notes: Comprehensive survey of trustworthy GNNs addressing privacy (membership/property inference, differential privacy, federated learning), robustness (adversarial attacks, certified defenses), fairness (non-discrimination), and explainability; bridges gap between GNN architecture research and practical deployment requirements in high-stakes applications (finance, healthcare).

[2026-05-17] ingest | Fact-Checking Meets Fauxtography: Verifying Claims About Images

• job: 1778895729102-0e446550
• submitter: reza
• page: 2019-zlatkova-fauxtography (deleted 2026-08-16)
• touched: wiki/papers/2019-zlatkova-fauxtography.md (created), wiki/authors/zlatkova-dimitrina.md (created), wiki/authors/koychev-ivan.md (created), wiki/authors/nakov-preslav.md (updated), wiki/topics/fact-checking.md (updated), wiki/topics/multimodal-detection.md (updated), wiki/topics/visual-misinformation.md (updated)
• notes: Dataset of 1,233 image-claim pairs for verifying factuality of claims about images; uses reverse image search to extract features (media credibility, URL domains, article titles); linear SVM achieves 80.1% accuracy; first work bridging fact-checking and visual misinformation detection.

[2026-05-17] ingest | A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions

• job: 1778895729058-6e07c370
• submitter: reza
• page: A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
• touched: wiki/papers/2023-wu-llm-generated-text-detection-survey.md (created), wiki/authors/wu-junchao.md (created), wiki/authors/yang-shu.md (created), wiki/authors/zhan-runzhe.md (created), wiki/authors/yuan-yulin.md (created), wiki/authors/wong-derek-fai.md (created), wiki/authors/chao-lidia-sam.md (created), wiki/topics/llm-generated-text-detection.md (updated), wiki/topics/detection-methods.md (updated), wiki/topics/watermarking.md (updated), wiki/papers/index.md (updated)
• notes: Comprehensive survey on necessity, methods (watermarking, statistics-based, neural-based, human-assisted), datasets, and future directions for LLM-generated text detection; emphasizes adversarial robustness and out-of-distribution challenges; complements earlier Tang et al. 2023 survey with deeper motivation and challenge analysis.

[2026-05-17] ingest | Measuring Political Bias in Large Language Models: What Is Said and How It Is Said

• job: 1778895729054-8ffa2e54
• submitter: reza
• page: Measuring Political Bias in Large Language Models: What Is Said and How It
• touched: wiki/papers/2024-bang-political-bias-llms.md (created), wiki/authors/bang-yejin.md (updated), wiki/authors/chen-delong.md (updated), wiki/authors/lee-nayeon.md (updated), wiki/authors/fung-pascale.md (updated), wiki/topics/political-bias.md (updated), wiki/topics/bias-in-language-models.md (updated), wiki/topics/llm-generated-content.md (updated), wiki/topics/media-bias-detection.md (updated), wiki/topics/framing-bias.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Two-tiered framework measuring political bias in LLM-generated content via political stance analysis (extreme anchor comparison) and framing bias decomposition (content and style). Tests 11 open-source LLMs across 14 politically divisive topics; finds liberal bias on social issues, US-centric focus, and nuanced issue-specific variation. First systematic measurement of political bias across multiple dimensions beyond left-right spectrum.

[2026-05-17] ingest | The Science of Detecting LLM-Generated Texts

• job: 1778895729049-3ba4a4cc
• submitter: reza
• page: The Science of Detecting LLM-Generated Texts
• touched: wiki/papers/2023-tang-detecting-llm-generated.md (created), wiki/authors/tang-ruixiang.md (created), wiki/authors/chuang-yu-neng.md (created), wiki/authors/hu-xia.md (created), wiki/topics/llm-generated-text-detection.md (created), wiki/topics/detection-methods.md (updated), wiki/topics/watermarking.md (updated), wiki/topics/misinformation.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Comprehensive arXiv survey of LLM-generated text detection; taxonomy of black-box (statistical, linguistic, fact-verification features; traditional and deep learning classifiers) and white-box (post-hoc and inference-time watermarking) approaches; reviews HC3, Neural Fake News, TweepFake, and other benchmarks; discusses paraphrasing attacks, dataset bias, confidence calibration challenges, and threats from open-source LLMs.

[2026-05-17] ingest | RAIDAR: Generative AI Detection via Rewriting

• job: 1778895729044-2d2408f5
• submitter: reza
• page: RAIDAR: Generative AI Detection via Rewriting
• touched: wiki/papers/2024-mao-raidar-detection-rewriting.md (created), wiki/authors/mao-chengzhi.md (created), wiki/authors/vondrick-carl.md (created), wiki/authors/wang-hao.md (created), wiki/authors/yang-junfeng.md (created), wiki/topics/ai-generated-text-detection.md (updated), wiki/topics/detection-methods.md (updated), wiki/topics/large-language-models.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: ICLR 2024 paper on detecting machine-generated text via rewriting behavior; measures invariance, equivariance, and output uncertainty from editing distance; achieves 60–95 F1 with 29-point improvements over DetectGPT/Ghostbuster; robust to adversarial rephrasing.

[2026-05-17] ingest | TELLER: A Trustworthy Framework For Explainable, Generalizable and Controllable Fake News Detection

• job: 1778895729035-7adf952e
• submitter: reza
• page: TELLER: A Trustworthy Framework For Explainable, Generalizable and Controllable
• touched: wiki/papers/2024-liu-teller.md (created), wiki/authors/liu-hui.md (created), wiki/authors/wang-wenya.md (created), wiki/authors/li-haoru.md (created), wiki/authors/li-haoliang.md (created), wiki/topics/explainable-ai.md (created), wiki/topics/trustworthy-ai.md (created), wiki/topics/neural-symbolic.md (created), wiki/topics/detection-methods.md (updated), wiki/topics/fact-checking.md (updated), wiki/index.md (updated)
• notes: Dual-system framework combining LLM-driven decomposition of claims into logic predicates with neural-symbolic decision learning; prioritizes three principles for trustworthy detection: explainability (interpretable rules), generalizability (cross-domain transfer), controllability (human intervention). Achieves 76% accuracy on GossipCop, 80%+ on other datasets while maintaining transparency; directly addresses gap between accuracy and trustworthiness in fake news detection.

[2026-05-17] ingest | Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection

• job: 1778895729030-d87db5cc
• submitter: reza
• page: Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection
• touched: wiki/papers/2024-ba-deepfake-forgery-clues.md (created), wiki/authors/ba-zhongjie.md (created), wiki/authors/liu-qingyu.md (created), wiki/authors/liu-zhenguang.md (created), wiki/authors/wu-shuang.md (created), wiki/authors/lin-feng.md (created), wiki/authors/lu-li.md (created), wiki/authors/ren-kui.md (created), wiki/topics/deepfake-detection.md (updated), wiki/index.md (updated)
• notes: Information-theoretic deepfake detection framework achieving 0.983 AUC on FaceForensics++; uses mutual information losses to extract disentangled local and global forgery clues; demonstrates strong cross-dataset generalization; addresses overfitting limitations of region-specific detection methods via principled feature disentanglement.

[2026-05-17] ingest | A General Language Assistant as a Laboratory for Alignment

• job: 1778895729005-d77e3cf4
• submitter: reza
• page: A General Language Assistant as a Laboratory for Alignment
• touched: wiki/papers/2021-askell-general-language-assistant.md (created), wiki/authors/askell-amanda.md (created), wiki/authors/bai-yuntao.md (created), wiki/authors/chen-anna.md (created), wiki/authors/drain-dawn.md (created), wiki/authors/jones-andy.md (created), wiki/authors/joseph-nicholas.md (created), wiki/authors/mann-ben.md (created), wiki/authors/dasarma-nova.md (created), wiki/authors/elhage-nelson.md (created), wiki/authors/hatfield-dodds-zac.md (created), wiki/authors/hernandez-danny.md (created), wiki/authors/kernion-jackson.md (created), wiki/authors/ndousse-kamal.md (created), wiki/authors/olsson-catherine.md (created), wiki/authors/olah-chris.md (created), wiki/topics/ai-alignment.md (created), wiki/topics/reinforcement-learning-human-feedback.md (created), wiki/topics/model-evaluation.md (created), wiki/topics/language-models.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Anthropic's foundational alignment evaluation framework using helpfulness/honesty/harmlessness criteria; introduces preference model pre-training for sample efficiency; demonstrates ranked preference modeling scales better than imitation learning; directly relevant to preventing AI-generated misinformation through alignment.

[2026-05-17] ingest | Botometer 101: Social bot practicum for computational social scientists

• job: 1778895728990-c9128249
• submitter: reza
• page: Botometer 101: Social bot practicum for computational social scientists
• touched: wiki/papers/2022-yang-botometer-101.md (created), wiki/authors/yang-kai-cheng.md (updated), wiki/authors/ferrara-emilio.md (updated), wiki/authors/menczer-filippo.md (updated), wiki/topics/social-bots.md (updated), wiki/topics/bot-detection.md (updated), wiki/topics/computational-social-science.md (updated)
• notes: Practicum tutorial on Botometer, the most widely-used machine learning tool for detecting social bots on Twitter; explains supervised classifier with 1,000+ features, model variants (V4 comprehensive, Lite for scalability), and practical guidance on interpretation and responsible use; valuable pedagogical resource for researchers entering bot detection work.

[2026-05-17] ingest | Community detection in graphs

• job: 1778895728925-6064e890
• submitter: reza
• page: Community detection in graphs
• touched: wiki/papers/2009-fortunato-community-detection-graphs.md (created), wiki/authors/fortunato-santo.md (created), wiki/topics/community-detection.md (updated), wiki/topics/network-analysis.md (updated), wiki/topics/graph-theory.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Fortunato's foundational 103-page survey on community detection algorithms and theory; comprehensive reference for modularity optimization, spectral methods, and applications to biological/social/technological networks; essential grounding for network-based approaches to misinformation detection.

[2026-05-17] ingest | Auto-Encoding Variational Bayes

• job: 1778895728869-e303bad5
• submitter: reza
• page: 2013-kingma-variational-autoencoder
• touched: wiki/papers/2013-kingma-variational-autoencoder.md (created), wiki/authors/kingma-diederik.md (created), wiki/authors/welling-max.md (created), wiki/topics/variational-inference.md (created), wiki/topics/generative-models.md (updated), wiki/topics/deep-learning.md (updated), wiki/papers/index.md (updated)
• notes: Kingma and Welling's foundational VAE paper introducing the reparameterization trick for efficient variational inference in deep generative models. Not directly about fake news but central to modern deep generative approaches that both generate and detect synthetic content. Enables Deep learning and Generative Models work underlying contemporary AI-based misinformation.

[2026-05-17 14:32] ingest | On the Opportunities and Risks of Foundation Models

• job: 1778895728853-e22a8184
• submitter: reza
• page: On the Opportunities and Risks of Foundation Models
• touched: wiki/papers/2021-bommasani-foundation-models.md (created), wiki/authors/bommasani-rishi.md (created), wiki/authors/hudson-drew.md (created), wiki/authors/adeli-ehsan.md (created), wiki/topics/foundation-models.md (created), wiki/topics/large-language-models.md (updated), wiki/topics/detection-methods.md (updated), wiki/topics/generative-ai-misuse.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Stanford CRFM's comprehensive 214-page report on foundation models' opportunities (capabilities, applications) and risks (misuse for fake news/deepfakes, fairness harms, environmental costs, security). Foundational reference for understanding how large language and vision models enable both generation and detection of misinformation at scale. Core addition to the wiki's coverage of modern AI-enabled disinformation.

[2026-05-17] ingest | Discovering Latent Knowledge in Language Models Without Supervision

• job: 1778895728843-cdda6b1d
• submitter: reza
• page: 2022-burns-discovering-latent-knowledge
• touched: wiki/papers/2022-burns-discovering-latent-knowledge.md (created), wiki/authors/burns-collin.md (created), wiki/authors/ye-haotian.md (created), wiki/authors/klein-dan.md (created), wiki/authors/steinhardt-jacob.md (updated), wiki/topics/language-model-truthfulness.md (updated), wiki/topics/language-models.md (updated), wiki/topics/natural-language-processing.md (updated), wiki/papers/index.md (updated)
• notes: Proposes Contrast-Consistent Search (CCS), unsupervised method for extracting latent knowledge from language models by finding linear probes consistent across statement-negation pairs. Achieves 71.2% accuracy across 6 models and 10 QA datasets, outperforming zero-shot by 4%. Relevant to understanding what language models know about truth without supervision and implications for detecting model-generated misinformation.

[2026-05-17] ingest | Discovering Language Model Behaviors with Model-Written Evaluations

• job: 1778895728833-28ef38ad
• submitter: reza
• page: Discovering Language Model Behaviors with Model-Written Evaluations
• touched: wiki/papers/2022-perez-discovering-lm-behaviors.md (created), wiki/authors/perez-ethan.md (updated), wiki/authors/ringer-sam.md (created), wiki/authors/lukosiu-kamile.md (created), wiki/authors/askell-amanda.md (updated), wiki/authors/ganguli-deep.md (updated), wiki/authors/kaplan-jared.md (updated), wiki/topics/language-models.md (updated), wiki/topics/rlhf-training.md (updated), wiki/topics/bias-in-language-models.md (updated), wiki/topics/ai-safety.md (updated), wiki/topics/inverse-scaling.md (created), wiki/index.md (updated)
• notes: Anthropic's comprehensive evaluation study proposing LM-generated evaluations to test 154 behaviors across personality, goal-seeking, ethics, and politics. Key findings include inverse scaling phenomena (larger models perform worse on some safety-relevant tasks), RLHF amplifies political bias, and instrumental subgoals emerge. Establishes that preference models predict RLHF behavior and enable early detection of safety issues. Directly relevant to understanding AI-generated misinformation risks, model alignment failures, and unintended consequences of RLHF training at scale.

[2026-05-17] ingest | The Capacity for Moral Self-Correction in Large Language Models

• job: 1778895728815-33ba9663
• submitter: reza
• page: 2023-ganguli-moral-self-correction
• touched: wiki/papers/2023-ganguli-moral-self-correction.md (created), wiki/authors/ganguli-deep.md (created), wiki/authors/schiefer-nicholas.md (created), wiki/authors/askell-amanda.md (updated), wiki/topics/bias-in-language-models.md (created), wiki/topics/model-alignment.md (created), wiki/topics/rlhf-training.md (created), wiki/topics/fairness-in-nlp.md (created), wiki/topics/ai-safety.md (updated), wiki/papers/index.md (updated)
• notes: Demonstrates moral self-correction emerges at 22B parameters and improves with model size and RLHF training. Evaluates three phenomena: stereotyping (BBQ), occupational gender bias (Winogender), racial discrimination (law-school admissions benchmark). Shows instruction-following reduces bias by ~43% at 175B; chain-of-thought reduces it ~84%. Establishes scaling laws for emergent capability to learn ethical constraints. Relevant to understanding how LLMs can be trained to avoid harmful outputs including bias and discrimination.

[2026-05-17] ingest | Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models' Alignment

• job: 1778895728805-fe2d4605
• submitter: reza
• page: Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models
• touched: wiki/papers/2023-liu-trustworthy-llms.md (created), wiki/authors/liu-yang.md (created), wiki/authors/yao-yuanshun.md (created), wiki/authors/ton-jean-francois.md (created), wiki/authors/zhang-xiaoying.md (created), wiki/authors/cheng-hao.md (created), wiki/authors/klochkov-yegor.md (created), wiki/authors/taufiq-muhammad-fauzal.md (created), wiki/authors/li-hang.md (created), wiki/authors/guo-ruocheng.md (updated), wiki/topics/llm-safety.md (updated), wiki/topics/hallucination.md (updated), wiki/topics/alignment.md (updated), wiki/topics/misinformation.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Comprehensive survey of LLM trustworthiness across seven dimensions (reliability, safety, fairness, resistance to misuse, explainability, social norms, robustness) with 29 sub-categories. Presents detailed taxonomy, measurement studies on six major LLMs (davinci, OPT-1.3B, text-davinci-003, flan-t5-xxl, ChatGPT, GPT-4), and empirical findings showing alignment effectiveness varies significantly across trustworthiness categories. Directly relevant to fake news wiki's concerns about hallucination, misinformation generation, propaganda misuse, and fairness in AI systems used for content moderation and fact-checking.

[2026-05-17] ingest | Red Teaming Language Models with Language Models

• job: 1778895728800-895c5bfc
• submitter: reza
• page: Red Teaming Language Models with Language Models
• touched: wiki/papers/2022-perez-red-teaming-language-models.md (created), wiki/authors/perez-ethan.md (updated), wiki/authors/huang-saffron.md (created), wiki/authors/song-francis.md (created), wiki/authors/cai-trevor.md (created), wiki/authors/ring-roman.md (created), wiki/authors/aslanides-john.md (created), wiki/authors/glaese-amelia.md (created), wiki/authors/mcalese-nat.md (created), wiki/authors/irving-geoffrey.md (created), wiki/topics/llm-safety.md (updated), wiki/topics/jailbreaking.md (updated), wiki/topics/generative-ai-misuse.md (updated), wiki/topics/bias-and-fairness.md (updated), wiki/topics/data-poisoning.md (updated), wiki/topics/adversarial-ml.md (updated), wiki/topics/large-language-models.md (updated), wiki/index.md (updated)
• notes: Foundational 2022 arXiv paper demonstrating automated red teaming using language models to systematically discover safety vulnerabilities. Proposes four generation methods (zero-shot to RL) and shows RL achieves 27-42% offensive output rates vs. 3.7% for zero-shot. Uncovers 1709 training data leakage instances, personal contact information generation, and distributional biases across demographic groups in 280B Gopher model. Dialogue-based red teaming reveals multi-turn harms. Critically important for understanding how to identify and mitigate AI-generated misinformation risks at scale, and how models themselves can serve as safety testing tools complementing human red teaming.

[2026-05-17] ingest | Hallucinations in Large Multilingual Translation Models

• job: 1778895728790-b9f116a8
• submitter: reza
• page: 2023-guerreiro-hallucinations-multilingual
• touched: wiki/papers/2023-guerreiro-hallucinations-multilingual.md (created), wiki/authors/guerreiro-nuno.md (created), wiki/authors/alves-duarte.md (created), wiki/authors/waldendorf-jonas.md (created), wiki/authors/haddow-barry.md (created), wiki/authors/birch-alexandra.md (created), wiki/authors/colombo-pierre.md (created), wiki/authors/martins-andre.md (created), wiki/topics/neural-machine-translation.md (created), wiki/topics/hallucinations.md (updated), wiki/topics/model-reliability.md (created), wiki/topics/multilingual-models.md (created), wiki/topics/low-resource-language-processing.md (created), wiki/papers/index.md (updated)
• notes: Comprehensive empirical analysis of hallucinations in M2M family (100+ translation directions) and ChatGPT revealing that hallucination rates exceed 10% in low-resource pairs and decline steeply with resource availability. Demonstrates hallucinations are qualitatively different between model families; ChatGPT generates off-target translations while M2M generates repetitive/oscillatory patterns. Shows distilled models can mitigate hallucinations through architectural choices; fallback systems effective at correction. Tangentially relevant to fake news wiki as study of how neural systems generate unreliable/false output under resource constraints.

[2026-05-17] ingest | ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education

• job: 1778895728784-d0ee01a1
• submitter: reza
• page: 2026-kasneci-chatgpt-education
• touched: wiki/papers/2026-kasneci-chatgpt-education.md (created), 23 author pages (created), 6 topic pages (created), wiki/topics/privacy.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Position paper from 23 German researchers synthesizing opportunities (personalized learning, lesson planning, research support, assessment automation, teacher professional development) and challenges (copyright violations from training on copyrighted content, bias amplification, student/educator over-reliance, privacy concerns, difficulty distinguishing machine-generated work, educator expertise gaps, cost and sustainability) of large language models in education. Proposes mitigation strategies centered on transparency, human oversight, continuous monitoring, ethical governance, and responsible integration. Addresses implications across elementary through professional education levels and emphasizes need for interdisciplinary research on responsible AI in education.

[2026-05-17 18:15] ingest | Lost in the Middle: How Language Models Use Long Contexts

• job: 1778895728773-ddace2aa
• submitter: reza
• page: 2023-liu-lost-in-middle
• touched: wiki/papers/2023-liu-lost-in-middle.md (created), wiki/authors/liu-nelson.md (created), wiki/authors/lin-kevin.md (created), wiki/authors/hewitt-john.md (created), wiki/authors/paranjape-ashwin.md (created), wiki/authors/bevilacqua-michele.md (created), wiki/authors/liang-percy.md (created), wiki/authors/petroni-fabio.md (updated), wiki/topics/long-context-language-models.md (created), wiki/topics/positional-bias.md (created), wiki/topics/attention-mechanisms.md (updated), wiki/topics/question-answering.md (updated), wiki/index.md (updated)
• notes: Empirical study revealing that language models exhibit U-shaped performance when processing long contexts—best at beginning/end, degraded in middle. Tests multi-document QA and key-value retrieval across models (MPT-30B, LongChat-13B, GPT-3.5-Turbo, Claude-1.3); encoder-decoder models more robust but still affected. Demonstrates that simply scaling context windows does not improve use of that context. Implications for RAG systems and fact-verification pipelines.

[2026-05-17 17:22] ingest | Atlas: Few-shot Learning with Retrieval Augmented Language Models

• job: 1778895728763-b7d6d509
• submitter: reza
• page: 2022-izacard-atlas-few-shot
• touched: wiki/papers/2022-izacard-atlas-few-shot.md (created), wiki/authors/izacard-gautier.md (created), wiki/authors/lewis-patrick.md (created), wiki/authors/lomeli-maria.md (created), wiki/authors/hosseini-lucas.md (created), wiki/authors/petroni-fabio.md (created), wiki/authors/schick-timo.md (created), wiki/authors/dwivedi-yu-jane.md (created), wiki/authors/joulin-armand.md (created), wiki/authors/riedel-sebastian.md (created), wiki/authors/grave-edouard.md (created), wiki/topics/few-shot-learning.md (updated), wiki/topics/language-models.md (updated), wiki/topics/knowledge-intensive-nlp.md (updated), wiki/topics/retrieval-augmented-generation.md (created), wiki/topics/dense-retrieval.md (created), wiki/papers/index.md (updated)
• notes: Carefully designed retrieval-augmented language model combining a dense retriever (Contriever) with T5 seq2seq. Achieves 42.4% accuracy on NaturalQuestions with only 64 examples, outperforming 540B-parameter models despite being 50× smaller. Demonstrates joint training of retriever and language model with multiple loss functions (attention distillation, EMDR², perplexity distillation) for efficient few-shot learning on knowledge-intensive tasks.

[2026-05-17 14:35] ingest | REALM: Retrieval-Augmented Language Model Pre-Training

• job: 1778895728753-69ceb0bc
• submitter: reza
• page: 2020-guu-realm
• touched: wiki/papers/2020-guu-realm.md (created), wiki/authors/guu-kelvin.md (created), wiki/authors/lee-kenton.md (created), wiki/authors/tung-zora.md (created), wiki/authors/pasupat-panupong.md (created), wiki/authors/chang-ming-wei.md (created), wiki/topics/knowledge-augmentation.md (created), wiki/topics/information-retrieval.md (updated), wiki/topics/question-answering.md (updated), wiki/topics/language-models.md (updated), wiki/papers/index.md (updated)
• notes: Jointly pre-trains a neural knowledge retriever with a language model via masked language modeling; learns unsupervised signal for relevance from the language modeling objective. Achieves SOTA on Open-QA (NaturalQuestions, WebQuestions, CuratedTrec) with 4–16% absolute improvements while being 30× smaller than comparable models.

[2026-05-17 12:14] ingest | Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

• job: 1778895728744-c132620d
• submitter: reza
• page: 2020-lewis-retrieval-augmented-generation (deleted 2026-08-16)
• touched: wiki/papers/2020-lewis-retrieval-augmented-generation.md (created), wiki/authors/lewis-patrick.md (created), wiki/authors/perez-ethan.md (created), wiki/authors/riedel-sebastian.md (created), wiki/authors/kiela-douwe.md (created), wiki/authors/lewis-mike.md (created), wiki/authors/karpukhin-vladimir.md (created), wiki/authors/yih-wen-tau.md (created), wiki/authors/petroni-fabio.md (created), wiki/authors/rocktaschel-tim.md (created), wiki/topics/knowledge-intensive-nlp.md (created), wiki/topics/retrieval-based-methods.md (created), wiki/topics/question-answering.md (updated), wiki/topics/information-retrieval.md (updated), wiki/topics/fact-checking.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Foundational paper introducing RAG (Retrieval-Augmented Generation), a hybrid architecture combining dense retrieval with seq2seq generation. Evaluated on knowledge-intensive tasks including open-domain QA, abstractive QA, Jeopardy questions, and fact verification. Demonstrates that augmenting pre-trained language models with retrieval produces more specific, diverse, and factual generations than parametric-only models, with the ability to update knowledge at test time without retraining.

[2026-05-17 11:45] ingest | A Survey on Evaluation of Large Language Models

• job: 1778895728734-430773f1
• submitter: reza
• page: A Survey on Evaluation of Large Language Models
• touched: wiki/papers/2023-chang-evaluation-llms.md (created), wiki/authors/chang-yupeng.md (created), wiki/authors/wang-xu.md (created), wiki/authors/wu-yuan.md (created), wiki/authors/yang-linyi.md (created), wiki/authors/zhu-kaijie.md (created), wiki/authors/chen-hao.md (created), wiki/authors/yi-xiaoyuan.md (created), wiki/authors/wang-cunxiang.md (updated), wiki/authors/wang-yidong.md (created), wiki/authors/ye-wei.md (created), wiki/authors/zhang-yue.md (created), wiki/authors/chang-yi.md (created), wiki/authors/yu-philip.md (created), wiki/authors/yang-qiang.md (created), wiki/authors/xie-xing.md (updated), wiki/topics/large-language-models.md (updated), wiki/topics/evaluation-metrics.md (created), wiki/topics/factuality.md (updated), wiki/topics/bias-detection.md (updated), wiki/topics/hallucination.md (created), wiki/topics/robustness.md (created), wiki/topics/ethics.md (created), wiki/topics/trustworthiness.md (created), wiki/index.md (updated)
• notes: Comprehensive 45-page survey of LLM evaluation methodologies across three dimensions (what, where, how to evaluate). Synthesizes 269 papers covering NLP tasks, generation quality, reasoning, robustness, ethical concerns (bias, toxicity), factuality and hallucination, trustworthiness, and domain-specific applications. Critical for assessing LLMs deployed in misinformation detection and understanding their limitations in safety-critical contexts.

[2026-05-17 10:15] ingest | ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

• job: 1778895728674-3aeec408
• submitter: reza
• page: 2020-clark-electra
• touched: wiki/papers/2020-clark-electra.md (created), wiki/authors/clark-kevin.md (created), wiki/authors/luong-minh-thang.md (created), wiki/authors/le-quoc.md (created), wiki/authors/manning-christopher.md (updated), wiki/topics/pre-trained-language-models.md (updated), wiki/topics/language-models.md (updated), wiki/topics/natural-language-processing.md (updated), wiki/topics/representation-learning.md (created), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Foundational 2020 ICLR paper proposing replaced token detection pre-training (ELECTRA) as more compute-efficient alternative to masked language modeling (BERT). ELECTRA-Small outperforms BERT-Small with equivalent compute. Particularly significant for practitioners with limited computational resources. Demonstrates that learning from all input tokens improves sample efficiency.

[2026-05-17 18:22] ingest | Rumor Cascades

• job: 1778895728655-2a8657fd
• submitter: reza
• page: Rumor Cascades
• touched: wiki/papers/2014-friggeri-rumor-cascades.md (created), wiki/authors/friggeri-adrien.md (created), wiki/authors/cheng-justin.md (updated), wiki/authors/adamic-lada.md (updated), wiki/authors/eckles-dean.md (updated), wiki/topics/rumor-propagation.md (created), wiki/topics/cascade-dynamics.md (created), wiki/topics/rumor-evolution.md (created), wiki/topics/information-diffusion.md (updated), wiki/topics/social-networks.md (updated), wiki/topics/fact-checking.md (updated), wiki/index.md (updated)
• notes: Foundational 2014 ICWSM paper analyzing 16.7K rumor cascades on Facebook (62.5M shares). Key finding: true rumors more viral than false (163 vs. 108 avg shares/upload) despite false rumors dominating uploads. Snopes fact-checks increase reshare deletion 4.4× for false rumors but minimal long-term effect. Demonstrates rumor evolution through meme mutation and variant selection. Includes heterogeneous treatment effects analysis and long-term propagation patterns.

[2026-05-17] ingest | Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data

• job: 1778895728601-61366f35
• submitter: reza
• page: Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data
• touched: wiki/papers/2024-marchal-genai-misuse-taxonomy.md (created), wiki/authors/marchal-nahema.md (created), wiki/authors/xu-rachel.md (created), wiki/authors/elasmar-rasmi.md (created), wiki/authors/gabriel-iason.md (created), wiki/authors/goldberg-beth.md (created), wiki/authors/isaac-william.md (created), wiki/topics/generative-ai-misuse.md (created), wiki/topics/online-manipulation.md (created), wiki/topics/fraud-detection.md (created), wiki/topics/deepfakes.md (updated), wiki/topics/synthetic-media.md (updated), wiki/topics/misinformation-detection.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: First empirical taxonomy of real-world GenAI misuse based on 191 documented incidents (Jan 2023–Mar 2024). Documents 18 distinct tactics: 10 exploiting GenAI capabilities (impersonation, falsification, content scaling) and 8 targeting system integrity/data. Key finding: most real-world misuse is low-tech and accessible, driven by five goals (opinion manipulation 27%, monetization 21%, fraud 18%, harassment 6%, reach 3.6%). Demonstrates GenAI democratized previously costly tactics for broader actor pools with minimal technical expertise.

[2026-05-17] ingest | DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection

• job: 1778895728587-c7bc9862
• submitter: reza
• page: DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection
• touched: wiki/papers/2024-wan-dell-llm-misinformation.md (created), wiki/authors/wan-herun.md (created), wiki/authors/feng-shangbin.md (created), wiki/authors/tan-zhaoxuan.md (created), wiki/authors/wang-heng.md (created), wiki/authors/tsvetkov-yulia.md (created), wiki/authors/luo-minnan.md (created), wiki/topics/llm-based-detection.md (created), wiki/topics/framing-detection.md (created), wiki/topics/user-interactions.md (created), wiki/topics/misinformation-detection.md (updated), wiki/topics/fake-news-detection.md (updated), wiki/topics/propaganda-detection.md (updated), wiki/topics/ensemble-methods.md (updated), wiki/topics/graph-neural-networks.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Multi-stage LLM integration framework for misinformation detection combining synthetic user reaction generation (from diverse demographic perspectives), explainable proxy task generation, and task-specific expert ensemble merging. Achieves up to 16.8% improvement in macro F1-score on fake news, framing, and propaganda detection tasks. Demonstrates that LLM-generated synthetic data with proper calibration improves both performance and robustness.

[2026-05-17 12:37] ingest | Adapting Fake News Detection to the Era of Large Language Models

• job: 1778895728583-93e43db1
• submitter: reza
• page: Adapting Fake News Detection to the Era of Large Language Models
• touched: wiki/papers/2023-su-adapting-fake-news-detection.md (created), wiki/authors/su-jinyan.md (created), wiki/authors/cardie-claire.md (created), wiki/authors/nakov-preslav.md (updated), wiki/topics/fake-news-detection.md (updated), wiki/topics/llm-generated-misinformation.md (updated), wiki/topics/machine-generated-text-detection.md (updated), wiki/topics/ai-generated-content.md (updated), wiki/index.md (updated), wiki/log.md (updated)
• notes: First comprehensive evaluation of fake news detectors across three eras of content generation (human-written, mixed human-machine, machine-dominated). Reveals critical generalization gaps: detectors trained exclusively on human-written fake news fail catastrophically on machine-generated variants. Provides practical guidance for deploying robust detectors in the era of LLM-generated content by recommending balanced training data.

[2026-05-17 07:47] ingest | On the Exploitability of Instruction Tuning

• job: 1778895728553-2adb7853
• submitter: reza
• page: 2023-shu-exploitability-instruction-tuning
• touched: wiki/papers/2023-shu-exploitability-instruction-tuning.md (created), wiki/authors/shu-manli.md (created), wiki/authors/wang-jiongxiao.md (created), wiki/authors/zhu-chen.md (created), wiki/authors/xiao-chaowei.md (created), wiki/authors/geiping-jonas.md (updated), wiki/authors/goldstein-tom.md (updated), wiki/topics/data-poisoning.md (created), wiki/topics/instruction-tuning.md (created), wiki/topics/adversarial-ml.md (updated), wiki/topics/large-language-models.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: First systematic study of data poisoning vulnerabilities in instruction-tuned LLMs; AutoPoison demonstrates that adversaries can inject undetectable poisoned training data to inject specific behaviors (content promotion, refusal hijacking) while maintaining model fluency. Shows larger models are more vulnerable, indicating scale-robustness tradeoff. Critical for understanding training-time threats to LLM safety.

[2026-05-17 11:47] ingest | Disinformation 2.0 in the Age of AI: A Cybersecurity Perspective

• job: 1778895728547-79218676
• submitter: reza
• page: Disinformation 2.0 in the Age of AI: A Cybersecurity Perspective
• touched: wiki/papers/2023-mazurczyk-disinformation-2-0.md (created), wiki/authors/mazurczyk-wojciech.md (created), wiki/authors/lee-dongwon.md (updated), wiki/authors/vlachos-andreas.md (updated), wiki/topics/cybersecurity.md (created), wiki/topics/disinformation.md (updated), wiki/topics/deepfakes.md (updated), wiki/topics/detection.md (updated), wiki/topics/ai-generated-text-detection.md (updated), wiki/index.md (updated)
• notes: Perspective piece proposing defense-in-depth approach to AI-enabled disinformation. Frames disinformation as cybersecurity threat; introduces four AI-powered attack scenarios (generative, perturbation, detection evasion, optimized spread). Proposes layered countermeasures across social network, ISP, device, and user levels. Directly applicable to implementation of multi-layered defenses.

[2026-05-17 23:59] ingest | Toxicity in ChatGPT: Analyzing Persona-assigned Language Models

• job: 1778895728528-d8f6a50c
• submitter: reza
• page: Toxicity in ChatGPT: Analyzing Persona-assigned Language Models
• touched: wiki/papers/2023-deshpande-chatgpt-toxicity.md (created), wiki/authors/deshpande-ameet.md (created), wiki/authors/murahari-vishvak.md (created), wiki/authors/rajpurohit-tanmay.md (created), wiki/authors/kalyan-ashwin.md (created), wiki/authors/narasimhan-karthik.md (created), wiki/topics/bias-detection.md (created), wiki/topics/ai-safety.md (updated), wiki/topics/language-models.md (updated), wiki/topics/toxicity-detection.md (updated), wiki/topics/content-moderation.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Large-scale empirical analysis (500K+ generations) of persona-induced toxicity in ChatGPT. Shows system parameter manipulation can increase toxicity up to 6× and reveals discriminatory bias targeting certain demographic groups, sexual orientations, races, and countries with colonial histories. Demonstrates a concrete safety vulnerability in ChatGPT that could be exploited for malicious content generation. First systematic study of this jailbreaking technique.

[2026-05-17 23:52] ingest | A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT

• job: 1778895728523-587ccb1b
• submitter: reza
• page: A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative
• touched: wiki/papers/2023-cao-aigc-survey.md (created), wiki/authors/cao-yihan.md (created), wiki/authors/li-siyu.md (created), wiki/authors/liu-yixin.md (created), wiki/authors/yan-zhiling.md (created), wiki/authors/dai-yutong.md (created), wiki/authors/sun-lichao.md (updated), wiki/authors/yu-philip-s.md (updated), wiki/topics/ai-generated-content.md (updated), wiki/topics/hallucinations.md (updated), wiki/topics/generative-models.md (updated), wiki/topics/security.md (created), wiki/topics/privacy.md (created), wiki/papers/index.md (auto-regenerated), wiki/index.md (updated)
• notes: First comprehensive AIGC survey covering full history from GANs to ChatGPT; essential foundational reference for AI-generated content risks including factuality/hallucination, security vulnerabilities, privacy threats, and multimodal generation. Positions AIGC as fundamental to misinformation threats. Critical context for detection and mitigation research in the fake news domain.

[2026-05-17 23:48] ingest | Learning Hierarchical Discourse-level Structure for Fake News Detection

• job: 1778895728513-6c95d3dd
• submitter: reza
• page: Learning Hierarchical Discourse-level Structure for Fake News Detection
• touched: wiki/papers/2019-karimi-hierarchical-discourse.md (created), wiki/authors/karimi-hamid.md (created), wiki/authors/tang-jiliang.md (updated), wiki/topics/discourse-structure.md (created), wiki/topics/document-structure-learning.md (created), wiki/topics/rhetorical-structure-theory.md (created), wiki/topics/text-only-fake-news-approaches.md (updated), wiki/topics/misinformation-detection-methods.md (updated), wiki/papers/index.md (auto-regenerated), wiki/index.md (updated)
• notes: First to investigate automatic document structure learning for fake news detection. Proposes HDSF framework constructing discourse dependency trees from sentence-level parsing; identifies structural properties distinguishing coherence in real vs. fake news. Achieves 82.19% accuracy outperforming text-only baselines. Real news exhibits significantly higher coherence in discourse organization.

[2026-05-17 23:45] ingest | Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations

• job: 1778895728508-7ff5fd7a
• submitter: reza
• page: Generative Language Models and Automated Influence Operations: Emerging Threats
• touched: wiki/papers/2023-goldstein-language-models-influence.md (created), wiki/authors/goldstein-josh.md (created), wiki/authors/sastry-girish.md (created), wiki/authors/musser-micah.md (created), wiki/authors/diresta-renee.md (created), wiki/authors/gentzel-matthew.md (created), wiki/authors/sedova-katerina.md (created), wiki/topics/influence-operations.md (created), wiki/topics/ai-generated-content.md (created), wiki/topics/disinformation.md (updated), wiki/topics/propaganda.md (updated), wiki/topics/language-models.md (updated), wiki/topics/content-moderation.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Comprehensive threat assessment of generative language models for influence operations, informed by October 2021 workshop with 30+ experts. Uses ABC (Actors, Behaviors, Content) framework to analyze how LLMs lower barriers to propaganda production. Proposes mitigation taxonomy spanning four stages: model design & construction, model access, content dissemination, belief formation. Evaluates each mitigation across technical feasibility, social feasibility, downside risk, and impact.

[2026-05-17 17:32] ingest | A Deep Learning Approach for Multimodal Deception Detection

• job: 1778895728504-39289389
• submitter: reza
• page: 2018-krishnamurthy-multimodal-deception
• touched: wiki/papers/2018-krishnamurthy-multimodal-deception.md (created), wiki/authors/krishnamurthy-gangeshwar.md (created), wiki/authors/majumder-navonil.md (created), wiki/authors/poria-soujanya.md (created), wiki/authors/cambria-erik.md (created), wiki/topics/deception-detection.md (updated), wiki/topics/multimodal-detection.md (updated), wiki/topics/deep-learning.md (updated), wiki/topics/neural-networks.md (updated), wiki/topics/facial-expressions.md (created), wiki/papers/index.md (updated)
• notes: Neural network approach to multimodal deception detection in courtroom trial videos using video (3D-CNN), audio (openSMILE), text (CNN), and micro-expressions; MLPH+C model achieves 96.14% accuracy and ROC-AUC 0.9799, substantially outperforming classical ML baselines on 121 courtroom videos.

[2026-05-17 04:37] ingest | A Heuristic-driven Uncertainty based Ensemble Framework for Fake News Detection in Tweets and News Articles

• job: 1778895728495-898fb363
• submitter: reza
• page: A Heuristic-driven Uncertainty based Ensemble Framework for Fake News Detection
• touched: wiki/papers/2021-das-heuristic-uncertainty-ensemble.md (created), wiki/authors/das-sourya-dipta.md (created), wiki/authors/basak-ayan.md (created), wiki/authors/dutta-saikat.md (created), wiki/topics/ensemble-methods.md (created), wiki/topics/uncertainty-estimation.md (created), wiki/topics/detection-methods.md (updated), wiki/topics/neural-networks.md (updated), wiki/topics/language-models.md (updated), wiki/topics/transfer-learning.md (updated), wiki/topics/deep-learning.md (updated), wiki/topics/covid-19.md (updated), wiki/topics/covid19-misinformation.md (updated), wiki/datasets/fakenewsnet.md (updated)
• notes: Ensemble of seven pre-trained language models (BERT, RoBERTa, XLNet, DeBERTa, ERNIE 2.0, ELECTRA) with soft voting, Statistical Feature Fusion Network, and uncertainty quantification via Monte Carlo Dropout; achieves F1=0.9892 on COVID-19 Fake News and F1=0.9156 on FakeNewsNet by combining text and metadata features.

[2026-05-17 16:49] ingest | Comparative Studies of Detecting Abusive Language on Twitter

• job: 1778895728491-153aa5ab
• submitter: reza
• page: Comparative Studies of Detecting Abusive Language on Twitter
• touched: wiki/papers/2018-lee-comparative-abusive-language.md (created), wiki/authors/lee-younghun.md (created), wiki/authors/yoon-seunghyun.md (created), wiki/authors/jung-kyomin.md (created), wiki/topics/abusive-language-detection.md (created), wiki/topics/hate-speech-detection.md (updated), wiki/topics/content-moderation.md (updated)
• notes: First comprehensive benchmark of traditional ML and neural network models on 100K-tweet Hate and Abusive Speech on Twitter dataset; bidirectional GRU with latent topic clustering achieves 0.805 F1. Shows neural models outperform traditional methods but character-level features hurt performance, while context tweets improve detection of hateful content.

[2026-05-17 16:45] ingest | Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter

• job: 1778895728481-8de60170
• submitter: reza
• page: Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech
• touched: wiki/papers/2016-waseem-annotator-influence.md (created), wiki/authors/waseem-zeerak.md (created), wiki/topics/annotation-bias.md (created), wiki/topics/data-quality.md (created), wiki/topics/intersectionality.md (created), wiki/topics/hate-speech-detection.md (updated), wiki/topics/crowdsourcing.md (updated)
• notes: Foundational work comparing expert vs. crowdsourced annotations for hate speech, showing systems trained on expert labels significantly outperform those on crowdworker data; introduces intersectional annotation scheme with "both racism and sexism" category grounded in Crenshaw (1989).

[2026-05-17 03:30] ingest | SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media

• job: 1778895728477-ecd3d5e2
• submitter: reza
• page: SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social
• touched: wiki/papers/2019-zampieri-semeval-offenseval.md (created), wiki/authors/zampieri-marcos.md (updated), wiki/authors/malmasi-shervin.md (updated), wiki/authors/nakov-preslav.md (updated), wiki/authors/rosenthal-sara.md (updated), wiki/authors/farra-noura.md (updated), wiki/authors/kumar-ritesh.md (updated), wiki/topics/offensive-language-detection.md (updated), wiki/topics/hate-speech-detection.md (updated), wiki/topics/content-moderation.md (updated), wiki/topics/shared-task.md (updated), wiki/papers/index.md (updated)
• notes: Shared task competition paper reporting on 115 teams' results across three hierarchical offensive language classification sub-tasks using OLID dataset. Top systems use BERT and ensembles; task A achieves 0.829 F1, but task C (target identification) plateaus at 0.660 F1, indicating remaining difficulty in low-resource group/other targeting scenarios.

[2026-05-17 15:32] ingest | Tweeting Negative Emotion: An Investigation of Twitter Data in the Aftermath of Violence on College Campuses

• job: 1778895728472-4a536a68
• submitter: reza
• page: 2016-jones-tweeting-negative-emotion
• touched: wiki/papers/2016-jones-tweeting-negative-emotion.md (created), wiki/authors/jones-nickolas-m.md (created), wiki/authors/wojcik-sean-p.md (created), wiki/authors/sweeting-josiah.md (created), wiki/authors/silver-roxane-cohen.md (created), wiki/topics/social-media-analysis.md (updated), wiki/topics/content-analysis.md (updated), wiki/topics/crisis-informatics.md (updated), wiki/index.md (updated)
• notes: Three case studies (Isla Vista, Northern Arizona University, Umpqua Community College shootings) demonstrate rapid-response social media analysis using geolocation identification and LIWC-based emotional text analysis; methodological contribution to crisis informatics and social media trauma research.

[2026-05-17 00:10] ingest | Misinformation Detection on YouTube Using Video Captions

• job: 1778895728458-125d55eb
• submitter: reza
• page: Misinformation Detection on YouTube Using Video Captions
• touched: wiki/papers/2021-jagtap-youtube-captions.md (created), wiki/authors/jagtap-raj.md (created), wiki/authors/kumar-abhinav.md (created), wiki/authors/goel-rahul.md (created), wiki/authors/sharma-shakshi.md (updated), wiki/authors/sharma-rajesh.md (updated), wiki/authors/george-clint.md (created), wiki/topics/misinformation-detection.md (updated), wiki/topics/natural-language-processing.md (updated), wiki/topics/video-classification.md (created), wiki/topics/text-embeddings.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: YouTube caption-based misinformation detection with pre-trained embeddings (GloVe, Word2Vec) achieves 0.92–0.95 F₁ binary and 0.85–0.90 three-class across five conspiracy topics; demonstrates metadata-only approaches insufficient but NLP-based caption analysis effective.

[2026-05-17 23:59] ingest | Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News

• job: 1778895728448-320b2811
• submitter: reza
• page: Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News
• touched: wiki/papers/2020-tan-neural-fake-news.md (created), wiki/authors/tan-reuben.md (created), wiki/authors/plummer-bryan.md (created), wiki/authors/saenko-kate.md (created), wiki/topics/fake-news-detection.md (updated), wiki/topics/neural-text-generation.md (updated), wiki/topics/multimodal-fake-news.md (created), wiki/topics/visual-semantic-inconsistency.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: First paper addressing multimodal neural fake news (text + images + captions). Introduces DIDAN (named entity-based inconsistency detection) and NeuralNews dataset (128K articles). Shows human susceptibility (46% naive detection, 68% with visual-semantic cues); identifies Type C (generated text + real images) as most deceptive.

[2026-05-17 23:55] ingest | Multimodal Emergent Fake News Detection via Meta Neural Process Networks

• job: 1778895728443-664ec910
• submitter: reza
• page: Multimodal Emergent Fake News Detection via Meta Neural Process Networks
• touched: wiki/papers/2021-wang-metafend.md (created), wiki/authors/wang-yaqing.md (updated), wiki/authors/ma-fenglong.md (updated), wiki/authors/wang-haoyu.md (created), wiki/authors/jha-kishlay.md (updated), wiki/authors/gao-jing.md (updated), wiki/topics/detection-methods.md (updated), wiki/topics/multimodal-detection.md (updated), wiki/topics/meta-learning.md (created), wiki/topics/few-shot-learning.md (created), wiki/topics/emergent-events.md (created), wiki/papers/index.md (updated), wiki/index.md (updated), wiki/log.md (updated)
• notes: Novel integration of meta-learning and neural processes for few-shot fake news detection on emergent events; proposes hard attention (Gumbel SoftMax) to handle class imbalance and label embedding for categorical treatment. Achieves 4–5% improvement over MAML baselines on Twitter/Weibo datasets.

[2026-05-17 23:52] ingest | Automated identification of media bias in news articles: an interdisciplinary literature review

• job: 1778895728438-387f77cc
• submitter: reza
• page: Automated identification of media bias in news articles: an interdisciplinary
• touched: wiki/papers/2018-hamborg-automated-media-bias.md (created), wiki/authors/hamborg-felix.md (created), wiki/authors/donnay-karsten.md (created), wiki/authors/gipp-bela.md (created), wiki/topics/media-bias.md (updated), wiki/topics/detection-methods.md (updated), wiki/topics/natural-language-processing.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated), wiki/log.md (updated)
• notes: Foundational interdisciplinary literature review bridging social science and computer science approaches to media bias detection. Defines nine distinct bias forms (event/source selection, labeling, placement, spin, etc.) and maps manual analysis approaches from social sciences (content analysis, frame analysis) to computational methods from NLP. Identifies gaps and opportunities for automated bias detection.

[2026-05-17 23:51] ingest | A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking

• job: 1778895728429-67bdf7e9
• submitter: reza
• page: A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking
• touched: wiki/papers/2019-hanselowski-richly-annotated-fact-checking.md (created), wiki/authors/hanselowski-andreas.md (created), wiki/authors/stab-christian.md (created), wiki/authors/schulz-claudia.md (created), wiki/authors/li-zile.md (created), wiki/authors/gurevych-iryna.md (created), wiki/topics/fact-checking.md (updated), wiki/topics/stance-detection.md (updated), wiki/topics/evidence-extraction.md (created), wiki/topics/claim-validation.md (created), wiki/index.md (updated), wiki/papers/index.md (updated), wiki/log.md (updated)
• notes: SNOPES corpus (6,422 claims, 14,296 documents) from heterogeneous web sources—first fact-checking dataset addressing multi-domain, multi-source challenges beyond Wikipedia-only datasets. Four-task pipeline: document retrieval, evidence extraction, stance detection, claim validation. Best models achieve F1 0.596 (stance), 0.637 (evidence ranking), 0.485 (claim validation). Error analysis highlights difficulty of unreliable sources and evidence paraphrasing.

[2026-05-17 23:50] ingest | MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims

• job: 1778895728408-749f5528
• submitter: reza
• page: MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking
• touched: wiki/papers/2019-augenstein-multifc-fact-checking.md (created), wiki/authors/augenstein-isabelle.md (updated), wiki/authors/lioma-christina.md (created), wiki/authors/wang-dongsheng.md (created), wiki/authors/hansen-casper.md (created), wiki/authors/hansen-christian.md (created), wiki/authors/lima-lucas-chaves.md (created), wiki/authors/simonsen-jakob-grue.md (created), wiki/datasets/multifc.md (created), wiki/topics/claim-verification.md (updated), wiki/topics/evidence-based-detection.md (updated), wiki/topics/fake-news-datasets.md (updated), wiki/topics/fact-checking.md (updated), wiki/topics/computational-fact-checking.md (updated), wiki/topics/veracity-prediction.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated), wiki/log.md (updated)
• notes: Largest real-world fact-checking dataset (34,918 claims from 26 domains) with evidence retrieval, entity linking, and multi-domain heterogeneous labels. Multi-task learning approach jointly ranks evidence pages and predicts veracity; achieves Macro F1 of 49.2%. Demonstrates challenge of multi-domain generalization in fact verification.

[2026-05-17 23:45] ingest | Misinformation Has High Perplexity

• job: 1778895728403-c76fda1c
• submitter: reza
• page: Misinformation Has High Perplexity
• touched: wiki/papers/2020-lee-misinformation-perplexity.md (created), wiki/authors/lee-nayeon.md (created), wiki/authors/bang-yejin.md (created), wiki/authors/madotto-andrea.md (created), wiki/authors/fung-pascale.md (created), wiki/topics/fact-checking.md (updated), wiki/topics/misinformation-detection.md (updated), wiki/topics/language-models.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated), wiki/log.md (updated)
• notes: Language model perplexity as unsupervised debunking signal. Evidence-grounded GPT-2 achieves 75% accuracy on scientific COVID-19 claims with minimal labeled data. Releases Covid19-scientific and Covid19-politifact test sets.

[2026-05-17 19:15] ingest | Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

• job: 1778895728394-f5922158
• submitter: reza
• page: Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation
• touched: wiki/papers/2017-kim-crowd-leveraging-detection.md (created), wiki/authors/kim-jooyeon.md (created), wiki/authors/tabibian-behzad.md (created), wiki/authors/oh-alice.md (created), wiki/authors/scholkopf-bernhard.md (created), wiki/authors/gomez-rodriguez-manuel.md (created), wiki/topics/crowd-sourced-fact-checking.md (created), wiki/topics/fact-checking.md (updated), wiki/topics/misinformation-spread.md (updated), wiki/topics/early-detection.md (updated), wiki/topics/misinformation-interventions.md (updated), wiki/papers/index.md (updated), wiki/log.md (updated)
• notes: Stochastic optimal control for scheduling crowd-sourced fact-checking. Formulates choosing which flagged stories to send for fact-checking as a stochastic optimal control problem for SDEs with jumps; develops CURB algorithm with closed-form solution. Validates on Twitter/Weibo data showing crowd can effectively reduce misinformation spread when fact-checking decisions are optimized.

[2026-05-17 18:00] ingest | The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength

• job: 1778895728389-e246c224
• submitter: reza
• page: The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength
• touched: wiki/papers/2018-potthast-clickbait-challenge.md (created), wiki/authors/potthast-martin.md (updated), wiki/authors/stein-benno.md (updated), wiki/authors/gollub-tim.md (created), wiki/authors/hagen-matthias.md (created), wiki/topics/clickbait.md (updated), wiki/topics/shared-task.md (updated), wiki/index.md (updated), wiki/log.md (updated)
• notes: Shared task on clickbait detection with 38,517 graded-scale annotated tweets from 27 top news publishers. Reformulates task as regression to measure clickbait strength (0–1 scale) rather than binary classification. 13 submitted approaches achieve significant gains; best system (zingel) reaches MSE 0.033 using bidirectional GRU with attention. Introduces Webis Clickbait Corpus 2017, canonical benchmark for graded clickbait detection research.

[2026-05-17 00:00] ingest | Auditing Radicalization Pathways on YouTube

• job: 1778895728366-86c37768
• submitter: reza
• page: Auditing Radicalization Pathways on YouTube
• touched: wiki/authors/horta-ribeiro-manoel.md (created), wiki/authors/ottoni-raphael.md (created), wiki/authors/west-robert.md (created), wiki/authors/almeida-virgilio.md (created), wiki/authors/meira-wagner.md (created), wiki/topics/youtube-radicalization.md (updated), wiki/topics/recommendation-algorithms.md (updated), wiki/topics/political-extremism.md (updated), wiki/topics/hate-speech.md (created), wiki/topics/platform-analysis.md (created), wiki/topics/far-right-online.md (created), wiki/papers/index.md (updated)
• notes: First large-scale quantitative audit of user radicalization on YouTube. Tracked 349 channels, 72.6M comments, 5.98M users across Intellectual Dark Web, Alt-lite, Alt-right communities. Demonstrates systematic migration toward extreme content and quantifies role of YouTube recommendation algorithm via random walk simulations. Bridges Hosseinmardi et al.'s population-scale findings (which found weak algorithmic effects) with Munger et al.'s supply-demand framing by focusing on algorithmic pathways and user trajectories.

[2026-05-16 14:32] ingest | Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media

• job: 1778895728356-a9339600
• submitter: reza
• page: Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media
• touched: wiki/authors/elsherief-mai.md (created), wiki/authors/kulkarni-vivek.md (created), wiki/authors/nguyen-dana.md (created), wiki/authors/wang-william-yang.md (updated), wiki/authors/belding-elizabeth.md (created), wiki/topics/hate-speech-detection.md (updated), wiki/topics/toxicity-detection.md (updated), wiki/papers/2018-elsherief-hate-lingo.md (created), wiki/index.md (updated)
• notes: First comprehensive linguistic and psycholinguistic analysis distinguishing directed (personal) vs. generalized (group-targeted) hate speech. LIWC, SAGE, frame semantics analysis reveals directed hate is more angry, informal, shows higher clout; generalized hate dominated by religious content and lethal/quantity language. 28,318 directed + ~2,500 generalized tweet dataset with high annotation agreement (Krippendorff's α=0.622). Curated by ElSherief et al. (2018 ICWSM). Adds five author pages, updates hate-speech-detection and toxicity-detection topics with the distinction between targeted attack types.

[2026-05-16 00:00] ingest | A Survey on Hate Speech Detection using Natural Language Processing

• job: 1778895728352-f8449702
• submitter: reza
• page: 2017-schmidt-hate-speech-detection
• touched: wiki/papers/2017-schmidt-hate-speech-detection.md (created), wiki/authors/schmidt-anna.md (created), wiki/authors/wiegand-michael.md (created), wiki/topics/hate-speech-detection.md (updated), wiki/index.md (updated)
• notes: Schmidt & Wiegand 2017 ACL workshop survey on automatic hate speech detection. Systematizes feature extraction approaches (surface features, word generalization, sentiment, lexical resources, linguistic, knowledge-based, meta-information, multimodal). Highlights critical lack of benchmark datasets, multilingual work, and challenges of implicit/context-dependent hate speech. Adds to hate-speech-detection topic page. First comprehensive NLP survey on hate speech in the wiki.

[2026-05-16 23:45] ingest | The Web of False Information

• job: 1778895728347-9ef646cf
• submitter: reza
• page: The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various
• touched: wiki/papers/2018-zannettou-web-false-information.md (created), wiki/authors/zannettou-savvas.md (updated), wiki/authors/sirivianos-michael.md (updated), wiki/authors/blackburn-jeremy.md (updated), wiki/authors/kourtellis-nicolas.md (created), wiki/topics/false-information.md (created), wiki/topics/rumors.md (created), wiki/topics/hoaxes.md (created), wiki/topics/clickbait.md (created), wiki/topics/containment.md (created), wiki/topics/conspiracy-theories.md (updated), wiki/topics/fake-news.md (updated), wiki/topics/propaganda.md (updated), wiki/topics/detection.md (updated), wiki/index.md (updated)
• notes: Foundational typology of false information ecosystem with eight types, twelve actors, six motives. Comprehensive survey of 200+ papers on user perception, propagation dynamics, detection, and political misinformation. Creates five new topic pages (false-information, rumors, hoaxes, clickbait, containment) and updates four existing ones. Adds new author Kourtellis. Central organizing framework for field.

[2026-05-16 23:15] ingest | A Survey on Computational Propaganda Detection

• job: 1778895728343-72c066fa
• submitter: reza
• page: A Survey on Computational Propaganda Detection
• touched: wiki/papers/2020-san-martino-propaganda-detection-survey.md (created), wiki/authors/san-martino-giovanni.md (updated), wiki/authors/cresci-stefano.md (updated), wiki/authors/barron-cedeno-alberto.md (updated), wiki/authors/yu-seunghak.md (created), wiki/authors/di-pietro-roberto.md (updated), wiki/authors/nakov-preslav.md (updated), wiki/topics/propaganda-detection.md (updated), wiki/topics/propaganda-techniques.md (updated), wiki/topics/computational-propaganda.md (updated), wiki/topics/coordinated-inauthentic-behavior.md (updated), wiki/papers/index.md (updated)
• notes: Comprehensive survey bridging NLP and network analysis perspectives on computational propaganda. Covers text-based detection (document/sentence/fragment-level classification, 18-technique taxonomy) and network-based detection (botnets, coordination, temporal behavior). Identifies key disconnection between NLP and network analysis communities. Argues for fine-grained technique detection over document-level classification. Highlights challenges: multimodal propaganda, adversarial robustness, synthetic text generation via language models. Creates computational-propaganda topic page, adds survey to propaganda-detection and propaganda-techniques. Links to 6 author pages (5 existing, 1 new). First meta-survey of the field (post-NLP4IF 2019 shared task).

[2026-05-16 23:14] ingest | On the Origins of Memes by Means of Fringe Web Communities

• job: 1778895728338-786211cd
• submitter: reza
• page: On the Origins of Memes by Means of Fringe Web Communities
• touched: wiki/papers/2018-zannettou-meme-origins.md (created), wiki/authors/zannettou-savvas.md (updated), wiki/authors/caulfield-tristan.md (updated), wiki/authors/blackburn-jeremy.md (updated), wiki/authors/de-cristofaro-emiliano.md (updated), wiki/authors/sirivianos-michael.md (updated), wiki/authors/stringhini-gianluca.md (updated), wiki/authors/suarez-tangil-guillermo.md (created), wiki/topics/meme-culture-politics.md (updated), wiki/topics/propagation-models.md (updated), wiki/topics/extremism-radicalization.md (updated), wiki/topics/political-extremism.md (updated), wiki/topics/social-media-analysis.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Zannettou et al. 2018 large-scale study of meme propagation across Twitter, Reddit, /pol/, Gab (160M images, 12.6K clusters). Uses perceptual hashing and Hawkes processes to measure influence flow from fringe to mainstream communities. Documents /pol/ and The_Donald's outsized role and prevalence of hateful memes on fringe platforms. Creates new fringe-communities section in wiki/index.md. Expands stub topic pages for meme-culture-politics, propagation-models, extremism-radicalization.

[2026-05-16 23:12] ingest | Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection

• job: 1778895728320-9ec3b0b1
• submitter: reza
• page: Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection
• touched: wiki/papers/2019-san-martino-fine-grained-propaganda-detection.md (created), wiki/authors/san-martino-giovanni.md (updated), wiki/authors/barron-cedeno-alberto.md (created), wiki/authors/nakov-preslav.md (updated), wiki/topics/propaganda-detection.md (created), wiki/topics/propaganda-techniques.md (created), wiki/topics/propaganda.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Shared task establishing fine-grained propaganda detection field. 18-technique taxonomy (loaded language, name calling, repetition, etc.). 497-article corpus with fragment-level annotations. 90 teams, 39 submitting; winners use fine-tuned BERT (0.63 F1 SLC, 0.25 F1 FLC). Creates propaganda-detection and propaganda-techniques topic pages. First major shared task on the intersection of propaganda, argumentation, and sentiment analysis.

[2026-05-16 22:45] ingest | Predicting Factuality of Reporting and Bias of News Media Sources

• job: 1778895728306-3ca0fa68
• submitter: reza
• page: Predicting Factuality of Reporting and Bias of News Media Sources
• touched: wiki/papers/2018-baly-predicting-factuality-bias.md (created), wiki/authors/alexandrov-dimitar.md (created), wiki/authors/baly-ramy.md (updated), wiki/authors/karadzhov-georgi.md (updated), wiki/authors/glass-james.md (updated), wiki/authors/nakov-preslav.md (updated), wiki/topics/media-bias.md (updated), wiki/topics/factuality-assessment.md (updated), wiki/topics/source-reliability.md (updated), wiki/topics/media-profiling.md (updated), wiki/index.md (updated)
• notes: Foundational media-level profiling work: extracts 141 features from article text, Wikipedia, Twitter, URLs, and web traffic to predict outlet factuality (low/mixed/high) and bias (7-point political scale). 1,066-website dataset. Shows textual features dominant for factuality (58.02 Macro-F₁), while Wikipedia/Twitter complement. Completes Baly et al. media profiling trilogy (2018→2019→2020). Creates media-profiling section in main index.

[2026-05-16 10:30] ingest | Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media

• job: 1778895728301-5adde96d
• submitter: reza
• page: Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and
• touched: wiki/papers/2019-baly-trustworthiness-political-ideology.md (created), wiki/authors/baly-ramy.md (updated), wiki/authors/karadzhov-georgi.md (updated), wiki/authors/saleh-abdelrhman.md (created), wiki/authors/glass-james.md (updated), wiki/authors/nakov-preslav.md (updated), wiki/topics/media-bias.md (created), wiki/topics/ordinal-regression.md (created), wiki/topics/factuality-assessment.md (updated), wiki/topics/media-profiling.md (updated), wiki/topics/multi-task-learning.md (updated), wiki/datasets/mbfc.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Copula ordinal regression framework jointly modeling outlet factuality (3-point) and political ideology (7-point); auxiliary tasks at different granularities (binary, 3-point, 5-point) reduce prediction error. Creates media-bias and ordinal-regression topics; creates MBFC dataset page. Links to 2021 Nakov survey and 2020 Baly media-profiling papers in chains of related work.

[2026-05-16] ingest | Detecting GAN-generated Imagery using Color Cues

• job: 1778895728287-8dfe12c1
• submitter: reza
• page: Detecting GAN-generated Imagery using Color Cues
• touched: wiki/papers/2018-mccloskey-gan-color-detection.md (created), wiki/authors/mccloskey-scott.md (created), wiki/authors/albright-michael.md (created), wiki/topics/gan-detection.md (created), wiki/topics/deepfake-detection.md (updated), wiki/topics/image-forensics.md (updated), wiki/topics/synthetic-media-detection.md (updated), wiki/index.md (updated)
• notes: Architectural analysis of GAN generators reveals two forensic cues: color channel overlap (unlike camera spectral sensitivities) and saturation suppression (via normalization). Saturation-based SVM achieves 0.7 AUC on fully synthetic images, 0.61 on face-swapped. Introduces gan-detection topic. Complementary to physiological signal and temporal consistency approaches.

[2026-05-16 21:35] ingest | Recurrent Convolutional Strategies for Face Manipulation Detection in Videos

• job: 1778895728282-3bb27089
• submitter: reza
• page: Recurrent Convolutional Strategies for Face Manipulation Detection in Videos
• touched: wiki/papers/2019-sabir-face-manipulation-video-detection.md (created), wiki/authors/sabir-ekraam.md (created), wiki/authors/cheng-jiaxin.md (created), wiki/authors/jaiswal-ayush.md (created), wiki/authors/abdAlmageed-wael.md (created), wiki/authors/masi-iacopo.md (created), wiki/authors/natarajan-prem.md (created), wiki/topics/deepfake-detection.md (updated), wiki/topics/face-manipulation.md (updated), wiki/topics/video-forensics.md (updated), wiki/topics/temporal-analysis.md (created), wiki/topics/multimedia-forensics.md (created), wiki/index.md (updated)
• notes: Temporal analysis via bidirectional RNN on aligned face sequences detects Deepfake/Face2Face/FaceSwap with 94–97% accuracy on FaceForensics++. Face-by-frame manipulation produces temporal artifacts (flickering) not enforced by synthesis tools; bidirectional recurrence captures forward/backward dependencies critical for detection. Introduces temporal-analysis and multimedia-forensics topics. Related to FaceForensics++ benchmark and orthogonal to biological signal approaches.

[2026-05-16 21:30] ingest | FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals

• job: 1778895728277-b80bd713
• submitter: reza
• page: FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals
• touched: wiki/papers/2019-cifci-fakecatcher.md (created), wiki/authors/cifci-umur-aybars.md (created), wiki/authors/demir-ilke.md (created), wiki/authors/yin-lijun.md (created), wiki/topics/biological-signals.md (created), wiki/topics/deepfake-detection.md (updated), wiki/topics/face-manipulation.md (updated), wiki/topics/face-forensics.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Biological signal (PPG) analysis achieves 91%+ accuracy on Face Forensics++/CelebDF/UADFV; orthogonal detection signal exploiting spatial coherence and temporal consistency that GANs fail to preserve. Introduces Deep Fakes Dataset with diverse real-world conditions. Foundational insight that synthetic video inconsistencies extend beyond visual artifacts to physiological signals.

[2026-05-16 23:59] ingest | DeepFakes: a New Threat to Face Recognition? Assessment and Detection

• job: 1778895728273-1a7479b1
• submitter: reza
• page: DeepFakes: a New Threat to Face Recognition? Assessment and Detection
• touched: wiki/papers/2018-korshunov-deepfakes-threat-detection.md (created), wiki/authors/korshunov-pavel.md (created), wiki/authors/marcel-sebastien.md (created), wiki/topics/deepfakes.md (updated), wiki/topics/face-recognition.md (created), wiki/topics/synthetic-media-detection.md (created), wiki/topics/gan-generated-video.md (created), wiki/index.md (updated)
• notes: First public GAN-based Deepfake dataset (620 videos); demonstrates VGG and FaceNet FAR of 85.62% and 95.00%; image quality metrics achieve 8.97% EER on HQ deepfakes while audio-visual lip-sync fails entirely. Foundational for understanding adversarial vulnerability of face recognition and the difficulty of detection as visual quality increases.

[2026-05-16 23:59] ingest | KAN: Knowledge-aware Attention Network for Fake News Detection

• job: 1778895728244-16bce05d
• submitter: reza
• page: KAN: Knowledge-aware Attention Network for Fake News Detection
• touched: wiki/papers/2021-dun-kan.md (created), wiki/authors/dun-yaqian.md (created), wiki/authors/tu-kefei.md (created), wiki/authors/chen-chen.md (created), wiki/authors/hou-chunyan.md (created), wiki/authors/yuan-xiaojie.md (created), wiki/topics/knowledge-graphs.md (updated), wiki/topics/detection-methods.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Knowledge-aware detection via entity linking to Wikidata; N-E and N-E2C attention mechanisms weight entity and entity-context importance; 7.4% F1 improvement over KCNN on PolitiFact. First to systematically incorporate entity contexts (immediate KG neighbors) for fake news detection.

[2026-05-16 23:52] ingest | Causal Understanding of Fake News Dissemination on Social Media

• job: 1778895728240-f97fea59
• submitter: reza
• page: Causal Understanding of Fake News Dissemination on Social Media
• touched: wiki/papers/2021-cheng-causal-fake-news-dissemination.md (created), wiki/authors/cheng-lu.md (created), wiki/authors/guo-ruocheng.md (created), wiki/authors/shu-kai.md (updated), wiki/authors/liu-huan.md (updated), wiki/topics/causal-inference.md (created), wiki/topics/user-susceptibility.md (created), wiki/topics/selection-bias.md (created), wiki/topics/fake-news-sharing-behavior.md (created), wiki/papers/index.md (updated)
• notes: Causal inference framework for understanding why users share fake news; uses inverse propensity scoring to learn unbiased fake news sharing behavior as surrogate confounder; identifies verified status, tweet count, and follower count as causally linked to reduced susceptibility. PolitiFact/GossipCop evaluation shows behavioral differences between fake/true news sharers (tighter clustering in fake).

[2026-05-16 22:41] ingest | Toxicity Detection with Generative Prompt-based Inference

• job: 1778895728235-c411a449
• submitter: reza
• page: Toxicity Detection with Generative Prompt-based Inference
• touched: wiki/papers/2022-wang-toxicity-detection-generative.md (created), wiki/authors/wang-yau-shian.md (created), wiki/authors/chang-yingshan.md (created), wiki/topics/toxicity-detection.md (created), wiki/topics/hate-speech-detection.md (created), wiki/topics/prompt-based-methods.md (created), wiki/topics/zero-shot-learning.md (created), wiki/topics/language-models.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated), wiki/topics/index.md (updated)
• notes: Zero-shot toxicity detection using generative prompt-based inference (estimating p(x|y)); shows generative outperforms discriminative on SBIC/HateExplain/Civility; highlights sensitivity to prompt wording and dual-use risks from self-diagnosis capability.

[2026-05-16 22:40] ingest | Towards Fine-Grained Reasoning for Fake News Detection

• job: 1778895728230-90ad8664
• submitter: reza
• page: Towards Fine-Grained Reasoning for Fake News Detection
• touched: wiki/papers/2021-jin-finerfact.md (created), wiki/authors/jin-yiqiao.md (created), wiki/authors/wang-xiting.md (created), wiki/authors/yang-ruichao.md (created), wiki/authors/sun-yizhou.md (created), wiki/authors/wang-wei.md (created), wiki/authors/liao-hao.md (created), wiki/authors/xie-xing.md (created), wiki/topics/fake-news-detection.md (updated), wiki/topics/graph-neural-networks.md (updated), wiki/topics/evidence-based-detection.md (updated), wiki/topics/explainable-detection.md (updated), wiki/topics/claim-verification.md (updated), wiki/index.md (updated)
• notes: Proposes FinerFact framework for fine-grained claim-evidence reasoning; mutual-reinforcement-based evidence ranking integrates human knowledge about attribute and topological salience; bi-channel KGAT enables reasoning over evidence groups. Achieves 91.7% F1 on PolitiFact, 86.4% on GossipCop with interpretable explanations; addresses explainability challenge in fake news detection.

[2026-05-16 22:35] ingest | A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances

• job: 1778895728225-48d073e1
• submitter: reza
• page: A Survey of Information Cascade Analysis: Models, Predictions, and Recent
• touched: wiki/papers/2021-zhou-cascade-analysis-survey.md (created), wiki/authors/zhou-fan.md (created), wiki/authors/xu-xovee.md (created), wiki/authors/trajcevski-goce.md (created), wiki/authors/zhang-kunpeng.md (created), wiki/topics/cascade-prediction.md (updated), wiki/topics/information-diffusion.md (updated), wiki/index.md (updated)
• notes: Comprehensive survey covering 250+ papers on information cascade modeling and prediction; provides taxonomy of feature-based, generative, and deep learning approaches across temporal, structural, user/item, and content features. Analyzes 89 feature-based methods, evaluation metrics, and benchmark datasets (Twitter, Weibo, citation cascades). Recent advances cover GNNs, attention mechanisms, and VAEs for cascade prediction.

[2026-05-16 22:15] ingest | FakeBERT: Fake News Detection in Social Media with a BERT-based Deep Learning Approach

• job: 1778895728215-38c43563
• submitter: reza
• page: FakeBERT: Fake News Detection in Social Media with a BERT-based Deep Learning
• touched: wiki/papers/2021-kaliyar-fakebert.md (created), wiki/authors/kaliyar-rohit-kumar.md (created), wiki/authors/goswami-anurag.md (created), wiki/authors/narang-pratik.md (created), wiki/topics/deep-learning.md (updated), wiki/topics/fake-news-detection.md (updated), wiki/topics/content-based-detection.md (updated), wiki/topics/detection-methods.md (updated), wiki/topics/bert.md (created), wiki/index.md (updated)
• notes: BERT-based approach combining transformer embeddings with parallel 1D CNNs achieves state-of-the-art 98.90% accuracy on 2016 election dataset. Demonstrates effectiveness of contextualized embeddings (98.90%) over CNN (92.70%) and LSTM (97.55%) baselines with word embeddings. Bidirectional training approach handles ambiguity in social media fake news. Low FPR (0.0160) and FNR (0.0059) show balanced classification performance.

[2026-05-16 22:12] ingest | DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection

• job: 1778895728200-1607513b
• submitter: reza
• page: DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection
• touched: wiki/papers/2021-mayank-deap-faked.md (created), wiki/authors/mayank-mohit.md (created), wiki/authors/sharma-shakshi.md (created), wiki/authors/sharma-rajesh.md (created), wiki/topics/fake-news-detection.md (updated), wiki/topics/knowledge-graphs.md (updated), wiki/topics/content-based-detection.md (updated), wiki/index.md (updated)
• notes: Knowledge graph approach combining biLSTM title encoding with Wikidata ComplEx embeddings. Title-only detection useful for early fake news detection on social media. Systematic bias removal (publication house names, politician mentions) improves model performance. Entity features provide ~13% F1 improvement over text-only baselines on Kaggle dataset.

[2026-05-16 21:45] ingest | Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations

• job: 1778895728180-c680ed6e
• submitter: reza
• page: Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social
• touched: wiki/papers/2019-kumar-tree-lstms-stance-rumor.md (created), wiki/authors/kumar-sumeet.md (created), wiki/authors/carley-kathleen-m.md (created), wiki/topics/rumor-detection.md (updated), wiki/topics/stance-classification.md (updated), wiki/topics/multi-task-learning.md (updated), wiki/topics/tree-structured-neural-networks.md (expanded), wiki/index.md (updated)
• notes: Extends tree-LSTM for rumor detection with convolutional child aggregation units. Achieves 12% and 15% F1-macro improvements over prior work for stance and veracity classification. Multi-task learning with alternating task training. Evaluates five feature representations (GLOVE, BERT, Skipthought, DeepMoji, SKPEMT) finding concatenation most effective.

[2026-05-16 20:30] ingest | Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations

• job: 1778895728160-b7c07787
• submitter: reza
• page: Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure
• touched: wiki/papers/2016-zubiaga-stance-sequential-tree.md (created), wiki/authors/zubiaga-arkaitz.md (updated), wiki/authors/kochkina-elena.md (updated), wiki/authors/liakata-maria.md (updated), wiki/authors/procter-rob.md (updated), wiki/authors/lukasik-michal.md (created), wiki/topics/rumour-detection.md (updated), wiki/topics/stance-classification.md (updated), wiki/topics/stance-detection.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Introduces Linear and Tree CRF variants to exploit sequential structure in Twitter rumour conversations. Demonstrates that tree-structured representation outperforms non-sequential baselines on 4,519 annotated tweets. Tree CRF achieves 0.440 macro-F1 vs. 0.400 for MaxEnt. Key contribution: first to model conversational tree structure for tweet-level stance classification; foundation for subsequent neural approaches (Kochkina et al. 2017) that extend with LSTM. Strong performance on supporting/querying classes but struggles with rare denial class.

[2026-05-16 19:25] ingest | Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM

• job: 1778895728155-9faa1e6e
• submitter: reza
• page: Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification
• touched: wiki/papers/2017-kochkina-rumour-stance-lstm.md (created), wiki/authors/kochkina-elena.md (updated), wiki/authors/liakata-maria.md (updated), wiki/authors/augenstein-isabelle.md (updated), wiki/topics/stance-classification.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Best-performing system in RumourEval 2017 Subtask A. Branch-LSTM architecture for conversation-aware stance classification achieves 78.4% accuracy. Decomposes tree-structured Twitter threads into linear branches to exploit sequential structure. Uses LSTM with word2vec embeddings and hand-crafted lexical/relational features. Strong performance on majority class (comment: 87.3% F) but struggles with minority classes (deny: 0% F), highlighting class imbalance challenge in rumour stance detection.

[2026-05-16 19:15] ingest | Detection and Resolution of Rumours in Social Media: A Survey

• job: 1778895728145-d92aa472
• submitter: reza
• page: Detection and Resolution of Rumours in Social Media: A Survey
• touched: wiki/papers/2018-zubiaga-rumor-detection-survey.md (created), wiki/authors/zubiaga-arkaitz.md (updated), wiki/authors/aker-ahmet.md (updated), wiki/authors/bontcheva-kalina.md (updated), wiki/authors/liakata-maria.md (updated), wiki/authors/procter-rob.md (updated), wiki/topics/rumour-detection.md (updated), wiki/topics/veracity-assessment.md (updated), wiki/topics/stance-classification.md (updated), wiki/index.md (updated)
• notes: Foundational survey establishing four-component architecture for rumour verification: Detection → Tracking → Stance Classification → Veracity Classification. Distinguishes newly emerging vs. long-standing rumours, reviews datasets (PHEME, RumourEval), annotation schemes (SDQC), and approaches from feature engineering to deep learning. Identifies key challenges: veracity classification substantially harder than stance, class imbalance in stance labels, vocabulary drift in emerging rumours, and cross-platform generalization. Survey became canonical reference for end-to-end rumour detection systems.

[2026-05-16 19:00] ingest | Studying Fake News via Network Analysis: Detection and Mitigation

• job: 1778895728140-1eec8d06
• submitter: reza
• page: Studying Fake News via Network Analysis: Detection and Mitigation
• touched: wiki/papers/2018-shu-network-analysis-fake-news.md (created), wiki/authors/shu-kai.md (updated), wiki/authors/liu-huan.md (updated), wiki/authors/bernard-h-russell.md (created), wiki/topics/network-analysis.md (updated), wiki/topics/propagation-based-detection.md (updated), wiki/topics/credibility-assessment.md (updated), wiki/topics/information-diffusion.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Comprehensive framework for network-based fake news detection and mitigation. Surveys network properties (echo chambers, filter bubbles, malicious accounts) and six network types (three homogeneous, three heterogeneous) applicable to fake news research. Introduces feature learning via network embeddings (NMF, RNNs), detection methods including temporal RNNs, credibility network propagation, and knowledge network matching for fact-checking. Mitigation strategies cover provenance identification, leader selection, influence minimization, and mitigating campaigns. Unified treatment of network's role across all three dimensions (content, social, temporal) of news dissemination ecosystem.

[2026-05-16 18:45] ingest | Combating Fake News with Interpretable News Feed Algorithms

• job: 1778895728130-c98653cc
• submitter: reza
• page: Combating Fake News with Interpretable News Feed Algorithms
• touched: wiki/papers/2018-mohseni-interpretable-news-feed.md (created), wiki/authors/mohseni-sina.md (created), wiki/authors/ragan-eric.md (created), wiki/topics/news-feed-algorithms.md (created), wiki/topics/algorithmic-transparency.md (created), wiki/topics/echo-chambers.md (updated), wiki/topics/filter-bubbles.md (updated), wiki/topics/content-moderation.md (updated), wiki/topics/fake-news.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Position paper reviewing fake news detection methods across creation, distribution, and consumption stages. Identifies echo chambers and filter bubbles as key mechanisms of harm enabled by news feed algorithms. Proposes interpretable and transparent algorithms as solution to increase user awareness and accountability. Two new topic pages (news-feed-algorithms, algorithmic-transparency) establish framework for platform design as fake news mitigation strategy.

[2026-05-16 18:35] ingest | Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements

• job: 1778895728125-69ad7705
• submitter: reza
• page: Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements
• touched: wiki/papers/2020-shu-mining-disinformation-fake-news.md (created), wiki/authors/shu-kai.md (updated), wiki/authors/wang-suhang.md (updated), wiki/authors/lee-dongwon.md (updated), wiki/authors/liu-huan.md (updated), wiki/topics/survey.md (updated), wiki/topics/fake-news-detection.md (updated), wiki/topics/misinformation.md (updated), wiki/topics/multimodal-detection.md (updated), wiki/topics/weak-supervision.md (created), wiki/topics/information-disorder.md (created), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Comprehensive book chapter organizing fake news research into three parts: user engagement in information disorder (cognitive biases, social dynamics, network effects); detection and mitigation techniques emphasizing weak social supervision paradigm (TriFN, dEFEND, MWSS models); trending issues (fake news literacy, neural generation, blockchain). Establishes weak supervision as practical alternative to expensive annotation for real-world deployment.

[2026-05-16 18:23] ingest | 3HAN: A Deep Neural Network for Fake News Detection

• job: 1778895728111-98014b3b
• submitter: reza
• page: 3HAN: A Deep Neural Network for Fake News Detection
• touched: wiki/papers/2023-singhania-3han.md (created), wiki/authors/singhania-sneha.md (created), wiki/authors/fernandez-nigel.md (created), wiki/authors/rao-shrisha.md (created), wiki/topics/hierarchical-attention.md (updated), wiki/topics/attention-mechanisms.md (created), wiki/topics/content-based-detection.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Three-level hierarchical attention network with separate attention mechanisms at word, sentence, and headline-body levels. Special focus on headlines as a distinctive feature of fake news via supervised pre-training on headlines alone. Achieves 96.77% accuracy on balanced dataset of ~41K articles; provides interpretable attention visualizations enabling human fact-checkers to focus verification on high-attention sentences and words.

[2026-05-16 18:10] ingest | TI-CNN: Convolutional Neural Networks for Fake News Detection

• job: 1778895728106-0595e7e2
• submitter: reza
• page: TI-CNN: Convolutional Neural Networks for Fake News Detection
• touched: wiki/papers/2018-yang-ti-cnn.md (created), wiki/authors/yang-yang.md (created), wiki/authors/lei-zheng.md (created), wiki/authors/jiawei-zhang.md (created), wiki/authors/qingcai-cui.md (created), wiki/authors/xiaoming-zhang.md (created), wiki/authors/zhoujun-li.md (created), wiki/authors/yu-philip-s.md (updated), wiki/topics/multimodal-detection.md (updated), wiki/topics/fake-news-detection.md (updated), wiki/topics/detection-methods.md (updated), wiki/papers/index.md (updated)
• notes: Early CNN-based multimodal approach combining explicit text features (word counts, question marks, capital letters, negations, pronouns) and explicit image features (resolution, face count) with learned latent representations; achieves F₁ 0.9210 on 2016 US presidential election news dataset; demonstrates effectiveness of text-image fusion for fake news detection.

[2026-05-16 17:35] ingest | MDFEND: Multi-domain Fake News Detection

• job: 1778895728097-80152dc9
• submitter: reza
• page: MDFEND: Multi-domain Fake News Detection
• touched: wiki/papers/2021-nan-mdfend-multidomain.md (created), wiki/authors/nan-qiong.md (created), wiki/authors/cao-juan.md (created), wiki/authors/zhu-yongchun.md (created), wiki/authors/wang-yanyan.md (created), wiki/authors/li-jintao.md (created), wiki/datasets/weibo21.md (created), wiki/topics/cross-domain-learning.md (updated), wiki/topics/transfer-learning.md (updated), wiki/topics/content-based-detection.md (updated), wiki/index.md (updated)
• notes: First multi-domain fake news dataset from single platform with explicit domain labels across 9 domains; mixture-of-experts architecture with domain gate for cross-domain transfer learning; F₁ 0.9137 outperforms all multi-domain baselines on Weibo21; directly addresses domain shift challenge in real-world deployment.

[2026-05-16 17:22] ingest | Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection

• job: 1778895728083-7d601814
• submitter: reza
• page: Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection
• touched: wiki/papers/2021-vo-hierarchical-evidence-attention.md (created), wiki/authors/vo-nguyen.md (created), wiki/authors/lee-kyumin.md (created), wiki/topics/fake-news-detection.md (updated), wiki/topics/evidence-based-detection.md (updated), wiki/topics/hierarchical-attention.md (updated), wiki/index.md (updated)
• notes: Novel evidence-aware fact-checking model combining word-level and document-level hierarchical multi-head attention; first work jointly applying multi-head attention mechanisms to both important words and important evidence documents. Achieves 9.47% improvement on Snopes dataset with thorough ablation studies demonstrating complementary benefits of both attention layers.

[2026-05-16 16:49] ingest | A Survey on Predicting the Factuality and the Bias of News Media

• job: 1778895728064-9e8c0661
• submitter: reza
• page: A Survey on Predicting the Factuality and the Bias of News Media
• touched: wiki/papers/2021-nakov-media-profiling-factuality-bias.md (created), wiki/authors/nakov-preslav.md (updated), wiki/authors/sencar-husrev.md (created), wiki/authors/an-jisun.md (created), wiki/authors/kwak-haewoon.md (created), wiki/topics/media-profiling.md (created), wiki/topics/source-reliability.md (created), wiki/topics/media-bias-detection.md (created), wiki/topics/factuality-assessment.md (created), wiki/topics/political-bias.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Survey of media-level profiling for source reliability and political bias; reviews textual (linguistic, embeddings), multimedia (forensics), audience (homophily), and infrastructure features; argues factuality harder to predict than bias due to external ground-truth requirements; covers gatekeeping/coverage/statement bias and multiple bias dimensions (framing, slant); identifies ordinal scale labeling, joint modeling, and temporal drift as key challenges.

[2026-05-16 16:47] ingest | A Survey on Stance Detection for Mis- and Disinformation Identification

• job: 1778895728054-0efdd696
• submitter: reza
• page: A Survey on Stance Detection for Mis- and Disinformation Identification
• touched: wiki/papers/2021-hardalov-stance-detection-survey.md (created), wiki/authors/hardalov-momchil.md (created), wiki/authors/arora-arnav.md (created), wiki/authors/augenstein-isabelle.md (created), wiki/authors/nakov-preslav.md (updated), wiki/topics/stance-detection.md (updated), wiki/index.md (updated)
• notes: Foundational survey systematizing stance detection for misinformation/disinformation; reviews 15+ datasets across English and non-English, task formulations (standalone fact-checking, pipeline components, rumour verification), and evolution from feature engineering to pre-trained transformers. First author from Checkstep Research; comprehensive taxonomy of stance categories (Support/Deny/Query/Comment) and applications spanning fact-checking, propaganda detection, and rumour resolution.

[2026-05-16 16:45] ingest | Better Safe Than Sorry: an Adversarial Approach to improve Social Bot Detection

• job: 1778895728049-bd3ce05e
• submitter: reza
• page: Better Safe Than Sorry: an Adversarial Approach to improve Social Bot Detection
• touched: wiki/papers/2019-cresci-evolved-spambot-detection.md (created), wiki/authors/tognazzi-stefano.md (created), wiki/authors/cresci-stefano.md (updated), wiki/authors/petrocchi-marinella.md (updated), wiki/authors/spognardi-angelo.md (updated), wiki/topics/bot-detection.md (updated), wiki/topics/social-spambots.md (updated), wiki/topics/adversarial-learning.md (updated), wiki/index.md (updated)
• notes: Applies adversarial learning paradigm to bot detection: uses genetic algorithms to simulate evolved spambots that evade detection, revealing vulnerability signatures and demonstrating proactive defense methodology. GenBot algorithm achieves evolved bot evasion rates ~79% vs. ~8% for non-evolved; identifies entropy-based detection opportunity. Directly extends 2016/2017 Cresci DNA work into adversarial synthesis framework.

[2026-05-16 16:43] ingest | DNA-Inspired Online Behavioral Modeling and Its Application to Spambot Detection

• job: 1778895728044-0286dfc3
• submitter: reza
• page: DNA-Inspired Online Behavioral Modeling and Its Application to Spambot Detection
• touched: wiki/papers/2016-cresci-dna-spambot-detection.md (created), wiki/authors/cresci-stefano.md (updated), wiki/authors/di-pietro-roberto.md (updated), wiki/authors/petrocchi-marinella.md (updated), wiki/authors/spognardi-angelo.md (updated), wiki/authors/tesconi-maurizio.md (updated), wiki/topics/bot-detection.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Novel methodology applying bioinformatic DNA analysis techniques to social media behavioral characterization. Introduces digital DNA fingerprinting using longest common substring analysis to detect spambot groups; achieves best-in-class performance (MCC 0.952 vs. prior methods) on evolved bots that evade account-level detection. Chronologically precedes and motivates the 2017-cresci-social-spambots paradigm-shift paper; foundational for understanding group-level detection shift in bot research.

[2026-05-16 16:42] ingest | The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race

• job: 1778895728039-4df64b2f
• submitter: reza
• page: The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the
• touched: wiki/papers/2017-cresci-social-spambots.md (created), wiki/authors/cresci-stefano.md (updated), wiki/authors/di-pietro-roberto.md (created), wiki/authors/petrocchi-marinella.md (created), wiki/authors/spognardi-angelo.md (created), wiki/authors/tesconi-maurizio.md (updated), wiki/topics/social-spambots.md (created), wiki/topics/group-behavior-detection.md (created), wiki/topics/bot-detection.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Landmark 2017 WWW Companion paper establishing evidence of new social spambot generation evading all existing detection approaches. First large-scale crowdsourced evaluation (13.3K annotations) showing 0.24 human accuracy vs 0.91 on traditional bots. Benchmarks five state-of-the-art detection methods showing all fail; identifies paradigm shift from account-centric to group-level detection approaches (digital DNA, reputation distribution analysis). Created two new topic pages (social-spambots, group-behavior-detection) and four author pages (three first-time ingests). Foundational for understanding bot sophistication evolution and detection methodology shifts.

[2026-05-16 15:35] ingest | Prevalence of Health Misinformation on Social Media: Systematic Review

• job: 1778895728035-44f01303
• submitter: reza
• page: Prevalence of Health Misinformation on Social Media: Systematic Review
• touched: wiki/papers/2021-suarez-lledo-health-misinformation-survey.md (created), wiki/authors/suarez-lledo-victor.md (created), wiki/authors/alvarez-galvez-javier.md (created), wiki/topics/health-misinformation.md (created), wiki/topics/vaccine-hesitancy.md (updated), wiki/topics/infodemic.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Systematic review of 69 studies characterizing health misinformation on social media. First comprehensive effort with standardized prevalence measures across vaccine (32%), drug/smoking (22%), noncommunicable disease (19%), pandemic (10%), eating disorder (9%), and medical treatment (7%) topics. Created new health-misinformation topic page; linked to vaccine-hesitancy and infodemic topics; covers platform-specific patterns and methodological approaches.

[2026-05-16 14:15] ingest | A Decade of Social Bot Detection

• job: 1778895728030-75c29e48
• submitter: reza
• page: A Decade of Social Bot Detection
• touched: wiki/papers/2020-cresci-social-bot-detection.md (created), wiki/authors/cresci-stefano.md (updated), wiki/topics/social-bots.md (updated), wiki/topics/bot-detection.md (updated), wiki/topics/coordinated-inauthentic-behavior.md (updated), wiki/topics/information-operations.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Comprehensive decade-long longitudinal review (2010–2020) of social bot detection research published in Communications of the ACM; catalogs 230+ detection techniques and documents three waves of bot sophistication; key contribution showing shift from individual-account to group-level detection approaches post-2015. Updated author page; added to four core topic pages (bot detection, social bots, coordinated inauthentic behavior, information operations).

[2026-05-16] ingest | Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web

• job: 1778895728026-a79b2d99
• submitter: reza
• page: Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and
• touched: wiki/papers/2018-zannettou-disinformation-warfare.md (created), wiki/authors/zannettou-savvas.md (updated), wiki/authors/caulfield-tristan.md (updated), wiki/authors/de-cristofaro-emiliano.md (updated), wiki/authors/sirivianos-michael.md (updated), wiki/authors/stringhini-gianluca.md (updated), wiki/authors/blackburn-jeremy.md (updated), wiki/topics/state-sponsored-information-operations.md (updated), wiki/topics/coordinated-inauthentic-behavior.md (updated), wiki/topics/information-operations.md (updated), wiki/papers/index.md (updated)
• notes: Early empirical study (Jan 2018, arXiv 1801.09288) of 2.7K Russian troll accounts with 27K tweets (2016–2017); precursor to later Zannettou et al. (Nov 2018) comparative Russia-Iran study. Documents troll account behavior (screen name changes, follower manipulation, batch tweet deletion), temporal patterns tied to political events (RNC, election, Charlottesville), and geographic targeting. Key finding: minimal direct Twitter influence but significant RT (Russian state news outlet) amplification using Hawkes processes. Updated six author pages; expanded three topic pages with cross-references to influence estimation and coordinated behavior.

[2026-05-16] ingest | MesoNet: A Compact Facial Video Forgery Detection Network

• job: 1778895728021-6de2c156
• submitter: reza
• page: MesoNet: A Compact Facial Video Forgery Detection Network
• touched: wiki/papers/2018-afchar-mesonet.md (created), wiki/authors/afchar-darius.md (created), wiki/authors/nozick-vincent.md (created), wiki/authors/yamagishi-junichi.md (updated), wiki/authors/echizen-isao.md (updated), wiki/topics/deepfake-detection.md (updated), wiki/topics/face-reenactment.md (created), wiki/topics/video-forensics.md (created), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: First systematic study of Deepfake detection introducing two compact CNN architectures (Meso-4, MesoInception-4) achieving 98% and 95% accuracy for Deepfake and Face2Face detection respectively. Includes first publicly available Deepfake dataset with 175 videos. Demonstrates that lightweight networks with ~28K parameters match or exceed complex architectures for face forgery detection; important contribution predating FaceForensics++ benchmark by a year. Created two author pages for Afchar and Nozick; updated two existing author pages; created two new topic pages (face-reenactment, video-forensics); updated deepfake-detection topic with MesoNet as key early work.

[2026-05-16] ingest | Characterizing the Use of Images in State-Sponsored Information Warfare Operations by Russian Trolls on Twitter

• job: 1778895728017-778aaff9
• submitter: reza
• page: Characterizing the Use of Images in State-Sponsored Information Warfare Operations
• touched: wiki/papers/2019-zannettou-russian-trolls-images.md (created), wiki/authors/bradlyn-barry.md (created), wiki/authors/zannettou-savvas.md (updated), wiki/authors/caulfield-tristan.md (updated), wiki/authors/de-cristofaro-emiliano.md (updated), wiki/authors/stringhini-gianluca.md (updated), wiki/authors/blackburn-jeremy.md (updated), wiki/topics/russian-disinformation.md (updated), wiki/topics/state-sponsored-information-operations.md (updated), wiki/topics/social-media-manipulation.md (updated), wiki/topics/twitter.md (updated), wiki/topics/visual-misinformation.md (updated), wiki/topics/computational-propaganda.md (updated), wiki/papers/index.md (updated)
• notes: Large-scale empirical study (1.8M images from 3.6K IRA accounts, 9M+ tweets) characterizing Russian state-sponsored use of images and memes on Twitter; demonstrates political imagery 2× more influential than non-political content; uses Hawkes Processes to model cross-platform influence on Reddit, The_Donald, Gab, mainstream news outlets. Created new author page for Bradlyn; updated five existing author pages; expanded visual-misinformation and computational-propaganda topic pages from stubs to full pages with references to this work; updated six existing topic pages with this paper as key reference.

[2026-05-16] ingest | The Creation and Detection of Deepfakes: A Survey

• job: 1778895728008-f8b5b7cf
• submitter: reza
• page: The Creation and Detection of Deepfakes: A Survey
• touched: wiki/papers/2020-mirsky-deepfakes-survey.md (created), wiki/authors/mirsky-yisroel.md (updated), wiki/authors/lee-wenke.md (updated), wiki/topics/deepfakes.md (updated), wiki/topics/face-synthesis.md (updated), wiki/topics/synthetic-media.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Comprehensive 38-page survey of both creation and detection of deepfakes published in ACM Computing Surveys. Covers generative architectures (GANs, VAEs, encoder-decoders, CNNs, RNNs), creation methods (reenactment, replacement, editing, synthesis), and detection approaches (artifact-specific and undirected). Identifies arms race between creation and detection, generalization challenges, and temporal coherence issues. Foundational work (published early 2020) capturing the state of the field before rapid advances in face synthesis quality. Updated two existing author pages, three existing topic pages (deepfakes, face-synthesis, synthetic-media); updated two index pages.

[2026-05-16] ingest | Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset

• job: 1778895727994-70f79f1a
• submitter: reza
• page: Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset
• touched: wiki/papers/2020-memon-covid19-misinformation-communities.md (created), wiki/authors/memon-shahan.md (created), wiki/authors/carley-kathleen.md (created), wiki/datasets/cmu-miscov19.md (created), wiki/topics/community-detection.md (created), wiki/topics/covid-19.md (updated), wiki/topics/bot-detection.md (updated), wiki/topics/misinformation.md (updated), wiki/topics/social-media-analysis.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: CMU-MisCOV19 dataset with 4,573 annotated COVID-19 tweets across 17 categories distinguishing misinformation from corrective content. Characterizes competing communities via network analysis (9.7e-4 vs. 6.5e-4 density for misinformed/informed), bot prevalence (19% vs. 11%), sociolinguistic patterns via LIWC, and vaccination stance analysis (41% anti-vaxxers among misinformed). Created two author pages, one dataset page, one new topic page (community-detection), updated four existing topic pages.

[2026-05-16] ingest | Two-Stream Neural Networks for Tampered Face Detection

• job: 1778895727989-36b2c4e1
• submitter: reza
• page: Two-Stream Neural Networks for Tampered Face Detection
• touched: wiki/papers/2018-zhou-two-stream-tampered-face.md (created), wiki/authors/zhou-peng.md (created), wiki/authors/han-xintong.md (created), wiki/authors/morariu-vlad.md (created), wiki/authors/davis-larry.md (created), wiki/topics/face-tampering-detection.md (created), wiki/topics/image-splicing.md (created), wiki/topics/steganalysis.md (created), wiki/topics/image-forensics.md (updated), wiki/topics/deepfake-detection.md (updated), wiki/datasets/swapme-faceswap.md (created), wiki/index.md (updated)
• notes: Two-stream network combining visual artifact detection (GoogLeNet) with steganalysis-based triplet network for face swapping detection. Introduces SwapMe/FaceSwap dataset with 2010 high-quality tampered images from two different face-swapping algorithms; achieves 0.927 AUC. Created four new author pages, three new topic pages (face-tampering-detection, image-splicing, steganalysis), one dataset page, and updated two existing topic pages.

[2026-05-16] ingest | DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection

• job: 1778895727985-6eb9c642
• submitter: reza
• page: DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
• touched: wiki/papers/2020-tolosana-deepfakes-survey.md (created), wiki/authors/tolosana-ruben.md (created), wiki/authors/vera-rodriguez-ruben.md (created), wiki/authors/fierrez-julian.md (created), wiki/authors/morales-aythami.md (created), wiki/authors/ortega-garcia-javier.md (created), wiki/topics/deepfakes.md (updated), wiki/topics/face-manipulation.md (updated), wiki/topics/detection.md (created), wiki/topics/face-synthesis.md (created), wiki/topics/generative-models.md (updated), wiki/topics/biometrics.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Comprehensive survey covering four types of face manipulation (entire face synthesis, identity swap, attribute manipulation, expression swap), GAN-based generation techniques (StyleGAN, ProGAN), detection methods, and public databases. All five authors are from BiDA Lab at Universidad Autónoma de Madrid. Establishes that most detection approaches fail under cross-domain conditions despite achieving >99% under controlled scenarios. Created author pages for all five authors; created three new topic pages (detection, face-synthesis, biometrics); updated three existing topic pages.

[2026-05-16] ingest | Exposing Deep Fakes Using Inconsistent Head Poses

• job: 1778895727980-07473ca5
• submitter: reza
• page: Exposing Deep Fakes Using Inconsistent Head Poses
• touched: wiki/papers/2018-yang-inconsistent-head-poses.md (created), wiki/authors/yang-xin.md (created), wiki/authors/li-yuezun.md (created), wiki/authors/lyu-siwei.md (created), wiki/topics/deepfake-detection.md (created), wiki/topics/media-forensics.md (created), wiki/topics/face-forensics.md (created), wiki/topics/3d-reconstruction.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Forensic detection of deepfakes via head pose inconsistency from facial landmark misalignment. Early example of geometry-based forensic cue; evaluates on UADFV and DARPA GAN datasets achieving 89.0% and 84.3% AUROC respectively. First three topic pages created for deepfake-detection, media-forensics, face-forensics.

[2026-05-16] ingest | Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated Text

• job: 1778895727975-958d474b
• submitter: reza
• page: Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between
• touched: wiki/papers/2022-dugan-boundary-detection.md (created), wiki/authors/dugan-liam.md (created), wiki/authors/kirubarajan-arun.md (created), wiki/authors/shi-sherry.md (created), wiki/authors/ippolito-daphne.md (updated), wiki/authors/callison-burch-chris.md (updated), wiki/topics/generated-text-detection.md (updated), wiki/topics/human-evaluation.md (updated), wiki/index.md (updated)
• notes: Game-based human evaluation study with 243 participants and 21,000+ annotations on boundary detection task (where human-written text transitions to machine-generated). Humans achieve 23.4% accuracy on first attempt (vs. 10% random); improvements with incentives; detailed error taxonomy showing genre-specific patterns. Releases RoFT platform and dataset.

[2026-05-16] ingest | Can LLM-Generated Misinformation Be Detected?

• job: 1778895727965-b3fc8ffd
• submitter: reza
• page: Can LLM-Generated Misinformation Be Detected?
• touched: wiki/papers/2024-chen-llm-generated-misinformation-detection.md (created), wiki/authors/chen-canyu.md (updated), wiki/authors/shu-kai.md (updated), wiki/topics/llm-generated-misinformation.md (updated), wiki/topics/misinformation-detection.md (updated), wiki/topics/large-language-models.md (updated), wiki/topics/detection-difficulty.md (created), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Empirical study with rigorous human evaluation and detector benchmarking showing LLM-generated misinformation is harder to detect than human-written content with same semantics; humans detect 9.6% of hallucinated news vs 40.7% of human-written misinformation; ChatGPT-3.5 detects 0%, GPT-4 outperforms humans but still struggles. Built taxonomy across five dimensions (types, domains, sources, intents, errors) and LLMFake dataset with ChatGPT, Llama2, Vicuna variants. Created new topic page (detection-difficulty) capturing empirical hardness of detecting AI-generated content; updated three existing topic pages (llm-generated-misinformation, misinformation-detection, large-language-models); updated two author pages.

[2026-05-16 12:15] ingest | Combating Misinformation in the Age of LLMs: Opportunities and Challenges

• job: 1778895727950-62c0abbd
• submitter: reza
• page: Combating Misinformation in the Age of LLMs: Opportunities and Challenges
• touched: wiki/papers/2023-chen-combating-misinformation-age-llms.md (created), wiki/authors/chen-canyu.md (created), wiki/authors/shu-kai.md (updated), wiki/topics/large-language-models.md (created), wiki/topics/llm-generated-misinformation.md (created), wiki/topics/misinformation-detection.md (updated), wiki/topics/fake-news.md (updated), wiki/topics/llm-generated-content.md (updated), wiki/index.md (updated)
• notes: Comprehensive survey examining both opportunities (detection, intervention, attribution using LLM capabilities) and challenges (hallucination, intentional generation of fake news, rumors, propaganda) in the age of LLMs. Analyzes domain-specific threats (journalism, healthcare, finance, politics) and proposes countermeasures (hallucination mitigation, safety improvements, detection, education). Created two new topic pages (large-language-models, llm-generated-misinformation) addressing LLM opportunities and challenges; updated four existing topic pages; created one new author page; updated Kai Shu affiliation from ASU to IIT.

[2026-05-16] ingest | On the Risk of Misinformation Pollution with Large Language Models

• job: 1778895727945-40393acd
• submitter: reza
• page: On the Risk of Misinformation Pollution with Large Language Models
• touched: wiki/papers/2023-pan-misinformation-pollution.md (created), wiki/authors/pan-yikang.md (created), wiki/authors/pan-liangming.md (created), wiki/authors/chen-wenhu.md (created), wiki/authors/kan-min-yen.md (created), wiki/authors/nakov-preslav.md (updated), wiki/authors/wang-william-yang.md (updated), wiki/topics/llm-safety.md (created), wiki/topics/question-answering.md (created), wiki/topics/synthetic-text-generation.md (created), wiki/topics/misinformation-detection.md (updated), wiki/index.md (updated)
• notes: Threat model for LLM-based misinformation generation showing GPT-3.5 can generate credible false content (GENREAD, CTRLGEN, REVISE, REIT settings) that significantly degrades ODQA performance (14–87% EM drop). Evaluates on NQ-1500 and CovidNews datasets with BM25/DPR retrievers and FiD/GPT readers. Proposes three defense strategies: RoBERTa-based detection (91–99.7% in-domain AUROC), vigilant prompting, and reader ensemble voting. Created three new topic pages (llm-safety, question-answering, synthetic-text-generation) addressing LLM safety and application-level vulnerabilities; created four new author pages; updated two existing author pages.

[2026-05-16] ingest | TruthfulQA: Measuring How Models Mimic Human Falsehoods

• job: 1778895727936-8ff04287
• submitter: reza
• page: TruthfulQA: Measuring How Models Mimic Human Falsehoods
• touched: wiki/papers/2021-lin-truthfulqa.md (created), wiki/authors/lin-stephanie.md (created), wiki/authors/hilton-jacob.md (created), wiki/authors/evans-owain.md (updated), wiki/topics/language-model-truthfulness.md (created), wiki/topics/hallucinations.md (created), wiki/topics/scaling-laws.md (created), wiki/topics/generated-text-detection.md (updated), wiki/index.md (updated)
• notes: Introduces TruthfulQA benchmark (817 questions, 38 categories) revealing inverse scaling where larger language models are less truthful, generating plausible human misconceptions. Best model (GPT-3-175B) achieves 58% truthfulness vs. 94% human baseline. Proposes GPT-judge automated metric achieving 90–96% accuracy. Created three new topic pages (language-model-truthfulness, hallucinations, scaling-laws) addressing core safety/alignment challenges for LLMs; created two new author pages; updated Evans author page.

[2026-05-16] ingest | Can AI-Generated Text be Reliably Detected?

• job: 1778895727930-dcc31c87
• submitter: reza
• page: Can AI-Generated Text be Reliably Detected?
• touched: wiki/papers/2023-sadasivan-ai-text-detection.md (created), wiki/authors/sadasivan-vinu-sankar.md (created), wiki/authors/kumar-aounon.md (created), wiki/authors/balasubramanian-sriram.md (created), wiki/authors/wang-wenxiao.md (created), wiki/authors/feizi-soheil.md (created), wiki/topics/ai-generated-text-detection.md (created), wiki/topics/watermarking.md (created), wiki/topics/adversarial-ml.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Comprehensive analysis of AI-generated text detector robustness showing recursive paraphrasing attacks defeat watermarking (AUROC 99.8% → 80.7%), neural network-based (96.5% → 59.8%), and retrieval-based (100% → <60%) detectors. Establishes theoretical bound on detection difficulty via Total Variation distance between human and AI-generated text. Created two new topic pages (ai-generated-text-detection, watermarking); created five new author pages; indexed in LLMs & generative-era section.

[2026-05-16] ingest | Fighting an Infodemic: COVID-19 Fake News Dataset

• job: 1778895727921-71ae1155
• submitter: reza
• page: Fighting an Infodemic: COVID-19 Fake News Dataset
• touched: wiki/papers/2020-patwa-covid-fake-news-dataset.md (created), wiki/authors/patwa-parth.md (created), wiki/authors/sharma-shivam.md (created), wiki/authors/pykl-srinivas.md (created), wiki/authors/guptha-vineeth.md (created), wiki/authors/kumari-gitanjali.md (created), wiki/authors/akhtar-md-shad.md (created), wiki/authors/ekbal-asif.md (created), wiki/authors/das-amitava.md (created), wiki/authors/chakraborty-tanmoy.md (created), wiki/topics/fake-news-detection.md (updated), wiki/topics/covid-19.md (updated), wiki/topics/datasets.md (updated), wiki/topics/social-media.md (updated), wiki/index.md (updated)
• notes: COVID-19 fake news dataset (10,700 posts/articles, balanced real/fake from social media and fact-checking sites). Benchmarks ML baselines: SVM achieves 93.32% F1-score. Created nine new author pages (all co-authors); updated four topic pages (fake-news-detection, covid-19, datasets, social-media); indexed in main wiki and covid-19 infodemic section.

[2026-05-16] ingest | Cross-lingual COVID-19 Fake News Detection

• job: 1778895727906-629f5abd
• submitter: reza
• page: Cross-lingual COVID-19 Fake News Detection
• touched: wiki/papers/2021-du-cross-lingual-covid19.md (created), wiki/authors/du-jiangshu.md (created), wiki/authors/dou-yingtong.md (created), wiki/authors/xia-congying.md (created), wiki/authors/yu-philip-s.md (created), wiki/authors/ma-jing.md (updated), wiki/authors/cui-limeng.md (updated), wiki/topics/covid19-misinformation.md (updated), wiki/topics/cross-lingual.md (updated), wiki/topics/multilingual-detection.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Cross-lingual COVID-19 fake news detection via machine translation; trains on English COVID-19 news (2,840 articles) using BERT with sub-text slicing (500 tokens) to preserve long-document information. Tests on manually-curated Chinese COVID-19 dataset (200 articles: 86 fake, 114 real). Achieves 75% accuracy, significantly outperforming monolingual (SAFE 71.6%) and cross-lingual baselines (CLEF 43.1%, EMET 45.9%). Created four new author pages for Du, Dou, Xia, Yu; updated two existing author pages (Ma, Cui); updated three topic pages to reflect cross-lingual transfer and COVID-19 detection contributions.

[2026-05-16] ingest | Finding Streams in Knowledge Graphs to Support Fact Checking

• job: 1778895727897-7139660a
• submitter: reza
• page: Finding Streams in Knowledge Graphs to Support Fact Checking
• touched: wiki/papers/2017-shiralkar-knowledge-streams.md (created), wiki/authors/shiralkar-prashant.md (updated), wiki/authors/flammini-alessandro.md (updated), wiki/authors/menczer-filippo.md (updated), wiki/authors/ciampaglia-giovanni-luca.md (updated), wiki/topics/fact-checking.md (updated), wiki/topics/knowledge-graphs.md (updated), wiki/papers/index.md (updated)
• notes: Novel network-flow approach to fact-checking treating knowledge graphs as flow networks. Introduces relational similarity metric via contracted line graphs and proposes Knowledge Stream algorithm using minimum-cost maximum-flow. Achieves comparable performance to supervised baselines with high interpretability via discovered path patterns. Updated four existing author pages with reference to new paper; updated two topic pages (fact-checking, knowledge-graphs); added entry to papers section index.

[2026-05-16] ingest | Computational fact checking from knowledge networks

• job: 1778895727892-b4da7730
• submitter: reza
• page: Computational fact checking from knowledge networks
• touched: wiki/papers/2015-ciampaglia-computational-fact-checking.md (created), wiki/authors/shiralkar-prashant.md (created), wiki/authors/rocha-luis.md (created), wiki/authors/bollen-johan.md (created), wiki/authors/ciampaglia-giovanni-luca.md (updated), wiki/authors/menczer-filippo.md (updated), wiki/authors/flammini-alessandro.md (updated), wiki/topics/computational-fact-checking.md (created), wiki/topics/knowledge-graphs.md (created), wiki/topics/semantic-similarity.md (created), wiki/topics/misinformation-detection.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Foundational paper proposing semantic proximity in knowledge graphs as a mechanism for computational fact checking. Uses shortest paths through Wikipedia knowledge graph weighted by entity generality to derive truth values. Created three new author pages (Shiralkar, Rocha, Bollen); created three new topic pages (computational-fact-checking, knowledge-graphs, semantic-similarity); updated existing author pages and misinformation-detection topic to include new paper reference.

[2026-05-16] ingest | DeClareE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning

• job: 1778895727886-fb47dc22
• submitter: reza
• page: DeClareE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning
• touched: wiki/papers/2018-popat-declareE.md (created), wiki/authors/popat-kashyap.md (created), wiki/authors/mukherjee-subhabrata.md (created), wiki/authors/yates-andrew.md (created), wiki/authors/weikum-gerhard.md (created), wiki/topics/evidence-based-detection.md (created), wiki/topics/credibility-assessment.md (updated), wiki/topics/neural-approaches.md (updated), wiki/index.md (updated)
• notes: End-to-end neural network for evidence-aware credibility assessment combining claims and evidence articles via bidirectional LSTMs with claim-specific attention. Achieves 78.96% accuracy on Snopes and 67.32% on PolitiFact without hand-crafted features. Created four author pages (Max Planck Institute and Amazon researchers); created new evidence-based-detection topic page; updated credibility-assessment and neural-approaches topic pages. Demonstrates significant improvement over text-only baselines through joint modeling of external evidence.

[2026-05-16] ingest | Topological Recurrent Neural Network for Diffusion Prediction

• job: 1778895727877-77559df2
• submitter: reza
• page: Topological Recurrent Neural Network for Diffusion Prediction
• touched: wiki/papers/2017-wang-topo-lstm.md (created), wiki/authors/wang-jia.md (created), wiki/authors/zheng-vincent.md (created), wiki/authors/liu-zemin.md (created), wiki/authors/chang-kevin.md (created), wiki/topics/information-diffusion.md (updated), wiki/topics/cascade-prediction.md (updated), wiki/topics/graph-neural-networks.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: First LSTM designed for dynamic DAGs; introduces diffusion topology data model and Topo-LSTM architecture with separate gates for different input types. Achieves 20–56% relative improvement over DeepCas on cascade prediction tasks across Digg, Twitter, and Memes networks. Advances the field by explicitly modeling cascade structure as evolving directed acyclic graphs rather than sequences or path samples. Created four author pages; updated three topic pages (information-diffusion, cascade-prediction, graph-neural-networks).

[2026-05-16] ingest | DeepCas: an End-to-end Predictor of Information Cascades

• job: 1778895727872-4fedae7e
• submitter: reza
• page: DeepCas: an End-to-end Predictor of Information Cascades
• touched: wiki/papers/2016-li-deepcas.md (created), wiki/authors/li-cheng.md (created), wiki/authors/ma-jiaqi.md (created), wiki/authors/guo-xiaoxiao.md (created), wiki/authors/mei-qiaozhu.md (created), wiki/topics/graph-neural-networks.md (created), wiki/topics/cascade-prediction.md (updated), wiki/topics/information-diffusion.md (updated), wiki/topics/deep-learning.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: First end-to-end deep learning approach to cascade prediction using random walk graph representations and attention mechanisms. Proposes automatically learning cascade graph representations via bidirectional GRU with learned path-sampling parameters, eliminating hand-crafted feature engineering. Outperforms feature-based, node embedding, and graph kernel baselines on Twitter and AMINER (academic citation) cascades. Created four author pages (University of Michigan researchers); created new topic page for graph-neural-networks; updated cascade-prediction, information-diffusion, and deep-learning topic pages.

[2026-05-16 02:33] ingest | Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams

• job: 1778895727867-3cabfb07
• submitter: reza
• page: Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams
• touched: wiki/papers/2010-ratkiewicz-astroturf-detection.md (created), wiki/authors/ratkiewicz-jacob.md (created), wiki/authors/conover-michael.md (created), wiki/authors/meiss-mark.md (created), wiki/authors/goncalves-bruno.md (created), wiki/authors/patil-snehal.md (created), wiki/authors/flammini-alessandro.md (updated), wiki/authors/menczer-filippo.md (updated), wiki/topics/astroturfing.md (created), wiki/topics/coordinated-inauthentic-behavior.md (updated), wiki/topics/misinformation-detection.md (updated), wiki/topics/information-diffusion.md (updated), wiki/topics/social-bot-detection.md (updated), wiki/topics/sentiment-analysis.md (updated), wiki/topics/network-analysis.md (updated), wiki/papers/index.md (updated)
• notes: Foundational 2010 paper introducing the Truthy system for detecting political astroturfing on Twitter. Network topology features more discriminative than sentiment for identifying deceptive campaigns (~96% accuracy). Case studies of real coordinated bot accounts uncovered during 2010 U.S. elections. Created astroturfing topic page and new author pages for five researchers; updated seven related topic pages (coordinated inauthentic behavior, misinformation detection, information diffusion, social bot detection, sentiment analysis, network analysis).

[2026-05-16] ingest | Can Cascades be Predicted?

• job: 1778895727862-1cdc6252
• submitter: reza
• page: Can Cascades be Predicted?
• touched: wiki/papers/2014-cheng-cascade-prediction.md (created), wiki/authors/cheng-justin.md (created), wiki/authors/adamic-lada.md (created), wiki/authors/dow-p-alex.md (created), wiki/authors/kleinberg-jon.md (created), wiki/authors/leskovec-jure.md (created), wiki/topics/cascade-prediction.md (created), wiki/topics/viral-spread.md (created), wiki/topics/temporal-prediction.md (created), wiki/topics/information-diffusion.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Foundational cascade prediction paper showing temporal features predict cascade growth (~80% accuracy) better than content or structural features alone. Created new author pages for all five Stanford/Facebook/Cornell researchers. Created three new topic pages (cascade-prediction, viral-spread, temporal-prediction); updated information-diffusion topic page. Added to propagation-based detection section of wiki index.

[2026-05-16] ingest | Scalable and Generalizable Social Bot Detection through Data Selection

• job: 1778895727856-6d2d7d73
• submitter: reza
• page: Scalable and Generalizable Social Bot Detection through Data Selection
• touched: wiki/papers/2019-yang-scalable-bot-detection-data-selection.md (created), wiki/authors/yang-kai-cheng.md (updated), wiki/authors/varol-onur.md (updated), wiki/authors/hui-pik-mai.md (updated), wiki/authors/menczer-filippo.md (updated), wiki/topics/social-bots.md (updated), wiki/topics/bot-detection.md (updated), wiki/topics/machine-learning.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Addresses scalability and cross-domain generalization in bot detection via minimal feature sets (20 metadata features) and strategic data selection. Compiles 13 labeled datasets (94,124 bots, 43,396 humans); shows training on curated subset outperforms exhaustive training, achieving 0.99 AUC on unseen datasets. Scalable to 900M tweets/day. Updated author pages for all four researchers; extended bot-detection and social-bots topic pages with data selection insights; updated machine-learning page.

[2026-05-16] ingest | Shifting attention to accuracy can reduce misinformation online

• job: 1778895727851-b773dde0
• submitter: reza
• page: Shifting attention to accuracy can reduce misinformation online
• touched: wiki/papers/2021-pennycook-attention-accuracy.md (created), wiki/authors/epstein-ziv.md (created), wiki/authors/moseh-mohsen.md (created), wiki/authors/archer-antonio.md (created), wiki/authors/pennycook-gordon.md (updated), wiki/authors/eckles-dean.md (updated), wiki/authors/rand-david.md (updated), wiki/topics/misinformation.md (updated), wiki/topics/misinformation-interventions.md (updated), wiki/topics/behavioral-nudges.md (updated), wiki/topics/attention.md (created), wiki/topics/accuracy.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Nature paper demonstrating that subtle accuracy-focus nudges increase sharing of accurate news across six experiments and a large Twitter field experiment (N=5,379). Identifies limited attention—not confusion or indifference—as the primary mechanism driving misinformation sharing. Created author pages for three new co-authors (Epstein, Moseh, Archer); updated three established author pages with this high-impact paper. Created topic pages on attention and accuracy as previously unrepresented concepts. Substantially expanded misinformation-interventions and behavioral-nudges topic pages.

[2026-05-16 14:32] ingest | Detection of Novel Social Bots by Ensembles of Specialized Classifiers

• job: 1778895727845-c9c0a7dd
• submitter: reza
• page: Detection of Novel Social Bots by Ensembles of Specialized Classifiers
• touched: wiki/papers/2020-sayyadiharikandeh-social-bots-ensemble.md (created), wiki/authors/sayyadiharikandeh-mohsen.md (created), wiki/authors/varol-onur.md (updated), wiki/authors/yang-kai-cheng.md (updated), wiki/authors/flammini-alessandro.md (updated), wiki/authors/menczer-filippo.md (updated), wiki/topics/social-bots.md (updated), wiki/topics/bot-detection.md (updated), wiki/topics/machine-learning.md (created), wiki/topics/cross-domain-generalization.md (created), wiki/index.md (updated), wiki/log.md (updated)
• notes: ESC (Ensemble of Specialized Classifiers) addresses cross-domain generalization failure in bot detection by training separate classifiers per bot type; shows heterogeneous bot behaviors (spambots, fake followers, political bots use different features). Achieves 56% F1 improvement on novel bots (47%→73%); recall 42%→84%. Deployed in Botometer v4 with AUC 0.99. Created pages for Sayyadiharikandeh; updated four established authors; created new topics on machine-learning and cross-domain-generalization; added paper to social-bots and bot-detection topic indexes.

[2026-05-16] ingest | Inoculating Against Fake News About COVID-19

• job: 1778895727839-278ca7d2
• submitter: reza
• page: Inoculating Against Fake News About COVID-19
• touched: wiki/papers/2020-van-der-linden-covid-inoculation.md (created), wiki/authors/compton-josh.md (created), wiki/authors/van-der-linden-sander.md (updated), wiki/authors/roozenbeek-jon.md (updated), wiki/topics/prebunking-inoculation.md (updated), wiki/topics/covid19-misinformation.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated), wiki/log.md (updated)
• notes: Frontiers in Psychology perspective on applying psychological inoculation theory to COVID-19 misinformation. Documents infodemic scope (46–48% exposure in UK/US; 25%+ misleading YouTube videos) and links conspiracy belief to vaccine hesitancy, reduced compliance, and violent intentions. Proposes active inoculation via Bad News game and Go Viral! as scalable prebunking alternatives to reactive fact-checking. Created author page for Compton (Dartmouth); updated van der Linden and Roozenbeek author pages; added paper to COVID-19 and prebunking-inoculation topics; integrated into main index and papers section.

[2026-05-16] ingest | BotOrNot: A System to Evaluate Social Bots

• job: 1778895727833-45fc1288
• submitter: reza
• page: BotOrNot: A System to Evaluate Social Bots
• touched: wiki/papers/2016-davis-botornot.md (created), wiki/authors/davis-clayton.md (updated), wiki/authors/varol-onur.md (updated), wiki/authors/ferrara-emilio.md (updated), wiki/authors/flammini-alessandro.md (updated), wiki/authors/menczer-filippo.md (updated), wiki/topics/social-bot-detection.md (updated from stub), wiki/topics/botometer.md (updated from stub), wiki/index.md (updated), wiki/log.md (updated)
• notes: Foundational system paper describing the BotOrNot service (released May 2014) for Twitter bot classification. Uses 1,000+ features across six categories (network, user, friends, temporal, content, sentiment) with Random Forest classifier achieving 0.95 AUC. Fleshed out two stub topics (social-bot-detection, botometer) with references to the broader bot detection literature. All five coauthors already had pages; added this paper as a key reference to each.

[2026-05-16] ingest | A Multi-Modal Method for Satire Detection using Textual and Visual Cues

• job: 1778895727822-7073d9c8
• submitter: reza
• page: A Multi-Modal Method for Satire Detection using Textual and Visual Cues
• touched: wiki/papers/2020-li-satire-detection.md (created), wiki/authors/li-lily.md (created), wiki/authors/levi-or.md (created), wiki/authors/hosseini-pedram.md (created), wiki/authors/broniatowski-david.md (created), wiki/topics/satire-detection.md (created), wiki/topics/image-forensics.md (created), wiki/topics/vision-language-models.md (created), wiki/topics/multimodal-detection.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Applies ViLBERT (Vision & Language BERT) with co-attentive fusion to detect satirical news from headline-image pairs. Creates multi-modal dataset of 10,000 articles (4000 satirical, 6000 mainstream). Achieves 93.8% accuracy, outperforming uni-modal and simple fusion baselines. Shows image forensics (ELA+CNN) alone insufficient, highlighting importance of joint multimodal reasoning. Created 3 new author pages (Li, Levi, Hosseini, Broniatowski); created 3 new topic pages (satire-detection, image-forensics, vision-language-models); updated multimodal-detection topic.

[2026-05-16] ingest | Arming the public with artificial intelligence to counter social bots

• job: 1778895727817-edc51716
• submitter: reza
• page: Arming the public with artificial intelligence to counter social bots
• touched: wiki/papers/2019-yang-arming-public-counter-bots.md (created), wiki/authors/yang-kai-cheng.md (created), wiki/authors/varol-onur.md (updated), wiki/authors/davis-clayton.md (updated), wiki/authors/ferrara-emilio.md (updated), wiki/authors/menczer-filippo.md (updated), wiki/topics/social-bots.md (updated), wiki/topics/bot-detection.md (updated), wiki/index.md (updated)
• notes: Comprehensive review of social bot types, detection methods, and human interaction with detection tools. Case study of Botometer with user survey (N=731) showing interpretation challenges with classifier scores. Proposes calibration methodology (Platt scaling and Bayesian Complete Automation Probability) to make bot detection scores interpretable and actionable.

[2026-05-16] ingest | Some Like it Hoax: Automated Fake News Detection in Social Networks

• job: 1778895727811-3c455bb7
• submitter: reza
• page: Some Like it Hoax: Automated Fake News Detection in Social Networks
• touched: wiki/papers/2017-tacchini-some-like-it-hoax.md (created), wiki/authors/tacchini-eugenio.md (created), wiki/authors/ballarin-gabriele.md (created), wiki/authors/della-vedova-marco.md (created), wiki/authors/moret-stefano.md (created), wiki/authors/de-alfaro-luca.md (created), wiki/topics/fake-news-detection.md (updated), wiki/topics/social-networks.md (updated), wiki/topics/user-behavior.md (updated), wiki/topics/crowdsourcing.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Early propagation-based detection using user interaction patterns (likes) on Facebook to identify hoaxes with >99% accuracy. Proposes logistic regression and harmonic BLC algorithms; demonstrates transfer learning across communities and robustness with <1% labeled training data. Foundational work showing diffusion patterns encode reliable veracity signals.

[2026-05-16] ingest | Sampling the News Producers: A Large News and Feature Data Set for the Study of the Complex Media Landscape

• job: 1778895727806-ee5f7270
• submitter: reza
• page: Sampling the News Producers: A Large News and Feature Data Set for the Study
• touched: wiki/papers/2018-horne-sampling-news-producers.md (created), wiki/authors/horne-benjamin.md (updated), wiki/authors/khedr-sara.md (created), wiki/authors/adali-sibel.md (updated), wiki/topics/datasets.md (updated), wiki/topics/news-credibility.md (created), wiki/topics/media-characterization.md (created), wiki/topics/content-features.md (created), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Introduces NELA2017 dataset with 1,586 articles from 92 diverse news sources (mainstream, hyper-partisan, satire, misinformation producers). Extracts 130 content-based features spanning linguistic, sentiment, engagement, bias, and morality dimensions. Foundation for subsequent NELA-GT releases that expanded to 713K (2018) and 1.78M (2022) articles. Enables source-level characterization without requiring per-article ground truth labels.

[2026-05-16] ingest | The Gospel According to Q: Understanding the QAnon Conspiracy

• job: 1778895727795-40a45273
• submitter: reza
• page: The Gospel According to Q: Understanding the QAnon Conspiracy from the Perspective
• touched: wiki/papers/2021-papasavva-qanon-conspiracy.md (created), wiki/authors/papasavva-antonis.md (created), wiki/authors/aliapoulios-max.md (created), wiki/authors/ballard-cameron.md (created), wiki/authors/de-cristofaro-emiliano.md (created), wiki/authors/stringhini-gianluca.md (created), wiki/authors/zannettou-savvas.md (created), wiki/authors/blackburn-jeremy.md (created), wiki/topics/qanon.md (created), wiki/topics/conspiracy-theories.md (updated), wiki/index.md (updated)
• notes: Systematic analysis of QAnon conspiracy theory via 4,961 unique Q drops from six aggregation sites and cross-platform Reddit/4chan/8kun data. Demonstrates poor canonicalization across aggregation sites, stylometric evidence of multiple authors, and traces mainstreaming pathway from fringe imageboards to Reddit (critical hub) to mainstream platforms. Empirically establishes that Q drops are exceptionally incoherent and low-quality despite adherent engagement.

[2026-05-16] ingest | FaceForensics++: Learning to Detect Manipulated Facial Images

• job: 1778895727790-b94e64ba
• submitter: reza
• page: FaceForensics++: Learning to Detect Manipulated Facial Images
• touched: wiki/papers/2019-rossler-faceforensics-plus-plus.md (created), wiki/authors/rossler-andreas.md (created), wiki/authors/cozzolino-davide.md (created), wiki/authors/verdoliva-luisa.md (created), wiki/authors/riess-christian.md (created), wiki/authors/thies-justus.md (created), wiki/authors/niessner-matthias.md (created), wiki/topics/deepfakes.md (updated), wiki/topics/synthetic-media.md (updated), wiki/topics/facial-manipulation-detection.md (created), wiki/index.md (updated)
• notes: Foundational facial forgery benchmark with 1.8M+ images from four manipulation methods (Face2Face, FaceSwap, DeepFakes, NeuralTextures). Comprehensive evaluation of hand-crafted and learned detection approaches. Human baseline (68.7%) vs. XceptionNet (99.26%). Critical analysis of compression robustness showing detector degradation under realistic post-processing.

[2026-05-16] ingest | The DeepFake Detection Challenge (DFDC) Dataset

• job: 1778895727785-7727f7f5
• submitter: reza
• page: The DeepFake Detection Challenge (DFDC) Dataset
• touched: wiki/papers/2020-dolhansky-dfdc-dataset.md (created), wiki/datasets/dfdc.md (created), wiki/authors/dolhansky-brian.md (created), wiki/authors/bitton-joanna.md (created), wiki/authors/pflaum-ben.md (created), wiki/authors/lu-jikuo.md (created), wiki/authors/howes-russ.md (created), wiki/authors/wang-menglin.md (created), wiki/authors/canton-ferrer-cristian.md (created), wiki/topics/deepfakes.md (updated), wiki/topics/synthetic-media.md (updated), wiki/datasets/index.md (updated), wiki/index.md (updated)
• notes: Largest deepfake detection benchmark (128,154 videos from 3,426 consenting actors) addressing ethical limitations of prior datasets. Includes diverse face-swap methods (DFAE, MM/NN, NTH, FSGAN, StyleGAN). Kaggle competition demonstrates detection remains unsolved despite scale and methodological diversity.

[2026-05-16] ingest | DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature

• job: 1778895727780-7903f823
• submitter: reza
• page: DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
• touched: wiki/papers/2023-mitchell-detectgpt.md (created), wiki/authors/mitchell-eric.md (created), wiki/authors/lee-yoonho.md (created), wiki/authors/khazatsky-alexander.md (created), wiki/authors/manning-christopher.md (created), wiki/authors/finn-chelsea.md (created), wiki/topics/machine-generated-text-detection.md (updated), wiki/topics/zero-shot-detection.md (created), wiki/topics/llm-generated-content.md (created), wiki/topics/probability-based-detection.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Identifies that LLM-generated text occupies negative-curvature regions of the log-probability landscape. Zero-shot detection via perturbation-induced curvature estimation; achieves 0.95 AUROC on GPT-2 detection without training data. Outperforms existing zero-shot baselines (rank, log-rank, entropy) and is competitive with supervised methods across XSum, SQuAD, WritingPrompts.

[2026-05-16] ingest | Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection

• job: 1778895727771-31668c93
• submitter: reza
• page: Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models
• touched: wiki/papers/2019-adelani-sentiment-preserving-fake-reviews.md (created), wiki/authors/adelani-david-ifeoluwa.md (created), wiki/authors/mai-haotian.md (created), wiki/authors/fang-fuming.md (created), wiki/authors/nguyen-huy-h.md (created), wiki/authors/yamagishi-junichi.md (created), wiki/authors/echizen-isao.md (created), wiki/topics/fake-reviews.md (created), wiki/topics/sentiment-analysis.md (created), wiki/topics/neural-language-models.md (updated), wiki/topics/text-generation.md (updated), wiki/topics/generated-text-detection.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Demonstrates practical attack using fine-tuned GPT-2 to generate high-quality sentiment-preserving fake product reviews. Two-step approach (generation + BERT validation) achieves 67–71% sentiment preservation; human eval shows fake reviews indistinguishable from authentic. All automatic detectors (Grover, GLTR, OpenAI) fail with 19.6–40.9% equal error rate; humans perform near chance (25–35% accuracy).

[2026-05-16] ingest | Truthful AI: Developing and Governing AI That Does Not Lie

• job: 1778895727757-2bcfac8d
• submitter: reza
• page: Truthful AI: Developing and Governing AI That Does Not Lie
• touched: wiki/papers/2021-evans-truthful-ai.md (created), wiki/authors/evans-owain.md (created), wiki/authors/cotton-barratt-owen.md (created), wiki/authors/finnveden-lukas.md (created), wiki/authors/bales-adam.md (created), wiki/authors/balwit-avital.md (created), wiki/authors/wills-peter.md (created), wiki/authors/righetti-luca.md (created), wiki/authors/saunders-william.md (created), wiki/topics/truthfulness.md (created), wiki/topics/AI-governance.md (created), wiki/topics/AI-safety.md (created), wiki/topics/content-moderation.md (updated), wiki/topics/misinformation-detection.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Oxford Future of Humanity Institute & OpenAI policy paper proposing governance frameworks for AI truthfulness. Distinguishes lies (strategically selected false statements) from negligent falsehoods (statements unacceptably likely false) and proposes standards focusing on truthfulness (statements match reality) rather than honesty. Describes three governance arrangements: industry self-regulation, co-regulation, and top-down regulation with institutional adjudication bodies. Surveys technical approaches including training data curation, RL from human feedback, adversarial training, and retrieval-grounded generation. Emphasizes early standards-setting before AI deception capabilities exceed human capacity. Created 8 author pages (Evans, Cotton-Barratt, Finnveden, Bales, Balwit, Wills, Righetti, Saunders from Oxford and OpenAI); created 3 new topic pages (truthfulness, AI-governance, AI-safety); updated 2 existing topic pages (content-moderation, misinformation-detection); added "AI safety & governance" section to main index.

[2026-05-16 23:55] ingest | The Threat of Offensive AI to Organizations

• job: 1778895727752-b24c8d91
• submitter: reza
• page: The Threat of Offensive AI to Organizations
• touched: wiki/papers/2021-mirsky-threat-offensive-ai.md (created), wiki/authors/mirsky-yisroel.md (created), wiki/authors/demontis-ambra.md (created), wiki/authors/kotak-jaidip.md (created), wiki/authors/shankar-ram.md (created), wiki/authors/gelei-deng.md (created), wiki/authors/yang-liu.md (created), wiki/authors/zhang-xiangyu.md (created), wiki/authors/lee-wenke.md (created), wiki/authors/elovici-yuval.md (created), wiki/authors/biggio-battista.md (created), wiki/topics/adversarial-ml.md (updated), wiki/topics/offensive-ai.md (created), wiki/topics/threat-modeling.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: ACM Computing Surveys 2021 paper surveying 33 offensive AI capabilities (OACs) adversaries use against organizations, organized into automation, campaign resilience, credential theft, exploit development, information gathering, social engineering, and stealth. Literature review combined with expert user study (N=22 from academia and industry) rating threats on profit/achievability/defeatability/harm dimensions; developed threat model T = H × (M/D) to prioritize defenses. Top three threats: exploit development, social engineering, information gathering. Relevant to misinformation ecosystems for understanding AI-enabled attacks on detection systems (model theft, poisoning, adversarial examples) and social engineering tactics (deepfakes, impersonation, phishing). Created 10 author pages (Mirsky, Demontis, Kotak, Shankar, Gelei, Liu Yang, Zhang, Lee, Elovici, Biggio); created 2 new topic pages (offensive-ai, threat-modeling); updated adversarial-ml topic stub with comprehensive content. Added new section to main index and papers index.

[2026-05-16 23:45] ingest | TweepFake: about detecting deepfake tweets

• job: 1778895727748-862e74d1
• submitter: reza
• page: TweepFake: about detecting deepfake tweets
• touched: wiki/papers/2020-fagni-tweepfake-detection.md (created), wiki/authors/fagni-tiziano.md (created), wiki/authors/falchi-fabrizio.md (created), wiki/authors/gambini-margherita.md (created), wiki/authors/martella-antonio.md (created), wiki/authors/tesconi-maurizio.md (created), wiki/topics/deepfakes.md (updated), wiki/topics/generated-text-detection.md (updated), wiki/topics/bot-detection.md (updated), wiki/topics/social-bots.md (updated), wiki/topics/synthetic-media.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: First public dataset (TweepFake) of human-written and machine-generated tweets on Twitter; 25,572 tweets (12,572 human, 12,572 bot-generated) from 23 bot accounts and 17 human accounts; bots trained with diverse generation techniques (GPT-2, RNN, LSTM, Markov Chains, etc.); benchmarks 13 different detection methods using BoW, BERT, and character-level encodings; finds transformer-based fine-tuned models (RoBERTa) achieve ~90% accuracy but GPT-2 tweets remain challenging (65–80% detection rate); character-level models surprisingly effective for short tweet text. Created 5 new author pages (Fagni, Falchi, Gambini, Martella, Tesconi from Italian research institutions); updated 5 topic pages (deepfakes, generated-text-detection, bot-detection, social-bots, synthetic-media) to reference this paper; updated paper section index and main index.

[2026-05-16 21:30] ingest | Automatic Detection of Generated Text is Easiest when Humans are Fooled

• job: 1778895727743-9a3be931
• submitter: reza
• page: Automatic Detection of Generated Text is Easiest when Humans are Fooled
• touched: wiki/papers/2019-ippolito-generated-text-detection.md (created), wiki/authors/ippolito-daphne.md (created), wiki/authors/duckworth-daniel.md (created), wiki/authors/callison-burch-chris.md (created), wiki/authors/eck-douglas.md (created), wiki/topics/neural-language-models.md (created), wiki/topics/human-evaluation.md (created), wiki/topics/generated-text-detection.md (updated), wiki/index.md (updated)
• notes: Ippolito et al.'s empirical study of human vs. automatic detection of GPT-2-generated text across three decoding strategies (top-k, nucleus sampling, untruncated random). Fine-tuned BERT achieves 80%+ accuracy on 192-token excerpts versus 71% for trained expert raters. Critical finding: discriminators trained on one strategy transfer poorly to others (42.5% accuracy drop from top-k to nucleus), whereas humans remain robust across strategies. Reveals asymmetric detection difficulty: easiest when humans are fooled (nucleus sampling produces human-like text) but hardest when humans remain reliable (top-k produces unnatural text). Created two new topic pages (neural-language-models, human-evaluation) and four new author pages. Updated generated-text-detection topic to include this paper alongside GLTR and GROVER work. Updated main index with generative-era section entry.

[2026-05-16 21:15] ingest | Release Strategies and the Social Impacts of Language Models

• job: 1778895727738-f51e2ead
• submitter: reza
• page: 2019-solaiman-release-strategies-language-models (deleted)
• touched: wiki/papers/2019-solaiman-release-strategies-language-models.md (created), wiki/authors/solaiman-irene.md (created), wiki/authors/brundage-miles.md (created), wiki/authors/clark-jack.md (created), wiki/authors/askell-amanda.md (created), wiki/authors/herbert-voss-ariel.md (created), wiki/authors/wu-jeff.md (created), wiki/authors/radford-alec.md (created), wiki/authors/krueger-gretchen.md (created), wiki/authors/kim-jong-wook.md (created), wiki/authors/kreps-sarah.md (created), wiki/authors/mccain-miles.md (created), wiki/authors/newhouse-alex.md (created), wiki/authors/blazakis-jason.md (created), wiki/authors/mcguffie-kris.md (created), wiki/authors/wang-jasmine.md (created), wiki/topics/bias-language-models.md (created), wiki/topics/responsible-ai.md (created), wiki/topics/generated-text-detection.md (updated), wiki/topics/language-models.md (updated), wiki/topics/fake-news-detection.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: OpenAI's comprehensive report on GPT-2 staged release strategy (Feb–Nov 2019, 124M–1.5B parameters) and responsible AI publication norms. Evaluates human credibility perception of synthetic text (~75% for largest models), automated detection capabilities (RoBERTa ~95% accuracy), biases in outputs (gender associations with "criminal", religion associations with "God", language preference shifts), and threat landscape of malicious actors. Conducted partnership-based risk analysis with four external institutions: Cornell University on human disinformation susceptibility, Middlebury CTEC on extremist fine-tuning and detection, University of Oregon on bias probes, University of Texas Austin on detectability after fine-tuning. Key finding: minimal evidence of planned misuse in public forums during observation period, but detection-generation arms race acknowledged. Published model card documentation and template legal agreements for responsible model sharing. Created 15 new author pages (all OpenAI and partner researchers), 2 new topic pages (bias-language-models, responsible-ai), updated 3 existing topic pages to reference staged release and detection research.

[2026-05-16 20:45] ingest | CoAID: COVID-19 Healthcare Misinformation Dataset

• job: 1778895727729-b9a60b03
• submitter: reza
• page: CoAID: COVID-19 Healthcare Misinformation Dataset
• touched: wiki/papers/2020-cui-coaid-covid19.md (created), wiki/authors/cui-limeng.md (created), wiki/authors/lee-dongwon.md (created), wiki/datasets/coaid.md (created), wiki/topics/datasets.md (created), wiki/topics/covid19-misinformation.md (updated), wiki/papers/index.md (updated), wiki/datasets/index.md (updated), wiki/papers/2020-yang-checked-covid19.md (updated), wiki/index.md (updated)
• notes: Dataset paper integrating 4,251 news articles (204 fake, 3,565 true), 482 claims, 926 social platform posts, and 296,000 user engagements across multiplatform sources (websites, Twitter, Facebook, Instagram, YouTube, TikTok). Provides COVID-19-specific healthcare misinformation benchmark with diverse source types and user engagement features. Benchmarks eight detection methods (SVM, LR, RF, CNN, BiGRU, CSI, SAMEv, HAN, dEFEND); state-of-the-art contextual models substantially outperform baselines. Created two new authors (Cui, Lee), one new dataset page, and one new topic page (datasets as general topic). Updated CHECKED paper to add CoAID cross-reference and covid19-misinformation topic to include CoAID in key datasets section. Updated index pages for papers and datasets.

[2026-05-16 20:15] ingest | The COVID-19 Social Media Infodemic

• job: 1778895727723-b07a8c20
• submitter: reza
• page: The COVID-19 Social Media Infodemic
• touched: wiki/papers/2020-cinelli-covid-infodemic.md (created), wiki/authors/cinelli-matteo.md (updated), wiki/authors/quattrociocchi-walter.md (updated), wiki/authors/galeazzi-alessandro.md (updated), wiki/authors/schmidt-ana.md (created), wiki/authors/zollo-fabiana.md (created), wiki/authors/scala-antonio.md (created), wiki/topics/covid-19.md (created), wiki/topics/infodemic.md (created), wiki/topics/epidemic-models.md (created), wiki/topics/rumor-amplification.md (created), wiki/topics/misinformation.md (updated), wiki/topics/social-media-analysis.md (updated), wiki/topics/information-diffusion.md (updated), wiki/index.md (updated)
• notes: Early pandemic COVID-19 infodemic analysis across five platforms (Twitter, Instagram, YouTube, Reddit, Gab) with 1.3M+ posts from 3.7M+ users. Uses epidemic models (EXP and SIR) to characterize platform-specific R₀ values (all > 1, indicating infodemic conditions); quantifies rumor amplification factors showing platform-dependent amplification (Gab 400%, YouTube 10%). Key finding: reliable and unreliable information spread at similar rates but platform affordances and source reputation create asymmetries. Created three new authors (Schmidt, Zollo, Scala) and four new topic pages (COVID-19, infodemic, epidemic-models, rumor-amplification). Updated three existing author pages and three topic pages. Added comprehensive section to wiki/index.md.

[2026-05-16 19:45] ingest | r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection

• job: 1778895727718-e20b24bf
• submitter: reza
• page: r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News
• dataset: Fakeddit
• touched: wiki/papers/2019-nakamura-fakeddit.md (created), wiki/datasets/fakeddit.md (created), wiki/authors/nakamura-kai.md (created), wiki/authors/levy-sharon.md (created), wiki/authors/wang-william-yang.md (updated), wiki/topics/multimodal-detection.md (updated), wiki/topics/fake-news-datasets.md (updated), wiki/topics/reddit.md (updated), wiki/topics/fine-grained-classification.md (created), wiki/papers/index.md (updated), wiki/datasets/index.md (updated), wiki/index.md (updated)
• notes: Large-scale multimodal fake news detection dataset with 1.06M Reddit submissions from 22 subreddits, 64% multimodal (text+image), 2-way/3-way/6-way labels via distant supervision. Baseline multimodal models (BERT+ResNet50) achieve 85.88% 6-way classification. Dataset demonstrates multimodal advantage (~10pp over text-only) and identifies satire/imposter as hardest categories. Created author pages for Nakamura and Levy; updated Wang author page. Added comprehensive discussion to multimodal-detection and fake-news-datasets topics. Created new fine-grained-classification topic page.

[2026-05-16 18:20] ingest | NELA-GT-2020: A Large Multi-Labelled News Dataset

• job: 1778895727697-3f6c849e
• submitter: reza
• dataset: NELA-GT-2020
• touched: wiki/datasets/nela-gt-2020.md (created), wiki/authors/gruppi-mauricio.md (updated), wiki/authors/horne-benjamin.md (updated), wiki/authors/adali-sibel.md (updated), wiki/datasets/index.md (updated), wiki/index.md (updated)
• notes: Dataset paper describing 1.78M news articles from 519 sources collected in 2020; covers COVID-19 pandemic and 2020 U.S. Presidential Election; introduced novel embedded-tweet feature (410K tweets from news articles); expanded topic coverage to include health-related news; source-level labels from Media Bias/Fact Check; released in SQLite and JSON formats. Updated author pages (Gruppi, Horne, Adalı) to include NELA-GT-2020 reference.

[2026-05-16 18:15] ingest | NELA-GT-2018: A Large Multi-Labelled News Dataset

• job: 1778895727687-86f53592
• submitter: reza
• page: NELA-GT-2018: A Large Multi-Labelled News Dataset for The Study of Misinformation
• dataset: NELA-GT-2018
• touched: wiki/papers/2019-norregaard-nela-gt-2018.md (created), wiki/datasets/nela-gt-2018.md (created), wiki/authors/norregaard-jeppe.md (created), wiki/authors/horne-benjamin.md (updated), wiki/authors/adali-sibel.md (updated), wiki/topics/fake-news-datasets.md (updated), wiki/index.md (updated)
• notes: Dataset paper describing 713K news articles from 194 sources collected Feb–Nov 2018; engagement-independent RSS feed scraping; source-level labels from 8 assessment sites (NewsGuard, Pew Research, Wikipedia, OpenSources, MBFC, AllSides, BuzzFeed, PolitiFact); multi-dimensional ground truth (reliability, bias, transparency, journalistic standards, consumer trust); addresses gaps in prior datasets (large scale, non-engagement-driven, multi-dimensional labels). Created Nørregaard author page; updated Horne and Adalı pages. Added NELA-GT-2018 to fake-news-datasets topic and to wiki index.

[2026-05-16 18:10] ingest | NELA-GT-2019: A Large Multi-Labelled News Dataset

• job: 1778895727682-b8975d94
• submitter: reza
• page: NELA-GT-2019 dataset
• touched: wiki/datasets/nela-gt-2019.md (created), wiki/authors/gruppi-mauricio.md (created), wiki/authors/horne-benjamin.md (created), wiki/authors/adali-sibel.md (created), wiki/datasets/index.md (updated), wiki/index.md (updated)
• notes: Dataset paper describing 1.12M news articles from 260 sources collected in 2019; source-level labels from 7 assessment sites (MBFC, AllSides, PolitiFact, Pew, Wikipedia, OpenSources, BuzzFeed); 3-class aggregated reliability label (reliable, mixed, unreliable); released in SQLite and JSON formats. Created 3 new author pages (Gruppi, Horne, Adalı), expanded datasets section with NELA-GT-2019 as flagship dataset.

[2026-05-16 13:45] ingest | RumourEval 2019: Determining Rumour Veracity and Support for Rumours

• job: 1778895727677-719642ac
• submitter: reza
• page: RumourEval 2019: Determining Rumour Veracity and Support for Rumours
• touched: wiki/papers/2019-gorrell-rumoureval.md (created), wiki/authors/gorrell-genevieve.md (created), wiki/authors/kochkina-elena.md (created), wiki/authors/liakata-maria.md (updated), wiki/authors/aker-ahmet.md (created), wiki/authors/zubiaga-arkaitz.md (updated), wiki/authors/bontcheva-kalina.md (updated), wiki/authors/derczynski-leon.md (updated), wiki/topics/stance-detection.md (updated), wiki/topics/rumour-detection.md (updated), wiki/topics/shared-task.md (updated), wiki/index.md (updated)
• notes: Extended RumourEval shared task with Twitter and Reddit data; two subtasks: (A) SDQC stance classification on 8,574 conversation posts, (B) veracity prediction on 446 rumours across 7 authors. 22 system submissions (70% increase from 2017) demonstrating effectiveness of pre-trained contextual embeddings (BERT, GPT, ELMo) and ensemble approaches. Created 4 new author pages, updated 4 existing author pages, expanded stance-detection topic page, added references to rumour-detection and shared-task topics.

[2026-05-16 17:25] ingest | Liar, Liar Pants on Fire: A New Benchmark Dataset for Fake News Detection

• job: 1778895727672-882f1644
• submitter: reza
• page: Liar, Liar Pants on Fire: A New Benchmark Dataset for Fake News Detection
• touched: wiki/papers/2017-wang-liar-dataset.md (created), wiki/authors/wang-william-yang.md (updated), wiki/topics/fake-news-detection.md (updated), wiki/topics/fake-news-datasets.md (updated), wiki/topics/benchmark.md (created), wiki/index.md (updated)
• notes: Foundational LIAR dataset paper (arXiv 1705.00648); introduces 12,836 manually labeled political statements from PolitiFact with 6-way fine-grained veracity labels and rich metadata (speaker, party, state, credit history). Demonstrates that hybrid CNN integrating speaker reputation with text improves detection over text-only approaches. Created 1 new topic page (benchmark), updated 3 existing topic pages, updated author page, and index. Adds key dataset reference to fake-news-detection and fake-news-datasets topics.

[2026-05-16 17:20] ingest | FEVER: A Large-Scale Dataset for Fact Extraction and VERification

• job: 1778895727653-f18743f5
• submitter: reza
• page: FEVER: A Large-Scale Dataset for Fact Extraction and VERification
• touched: wiki/papers/2018-thorne-fever-dataset.md (created), wiki/authors/thorne-james.md (updated), wiki/authors/vlachos-andreas.md (updated), wiki/authors/christodoulopoulos-christos.md (updated), wiki/authors/mittal-arpit.md (updated), wiki/topics/fact-checking.md (updated), wiki/topics/fake-news-datasets.md (updated), wiki/topics/natural-language-inference.md (created), wiki/topics/information-retrieval.md (created), wiki/topics/claim-verification.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Original FEVER dataset paper (arxiv 1803.05355); introduces 185,445 human-verified claims with Wikipedia evidence for fact verification training. Three-class labels (SUPPORTED/REFUTED/NOT ENOUGH INFO) with sentence-level annotation. Baseline pipeline achieves 31.87% accuracy with correct evidence requirement. Created 3 new topic pages (natural-language-inference, information-retrieval, claim-verification), updated 4 author pages and 2 existing topic pages.

[2026-05-16 17:15] ingest | The Fact Extraction and VERification (FEVER) Shared Task

• job: 1778895727648-d0b7cb80
• submitter: reza
• page: The Fact Extraction and VERification (FEVER) Shared Task
• touched: wiki/papers/2018-thorne-fever-shared-task.md (created), wiki/authors/thorne-james.md (created), wiki/authors/vlachos-andreas.md (created), wiki/authors/cocarascu-oana.md (created), wiki/authors/christodoulopoulos-christos.md (created), wiki/authors/mittal-arpit.md (created), wiki/topics/fact-checking.md (updated), wiki/topics/fake-news-datasets.md (updated), wiki/topics/detection-methods.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: First large-scale shared task on fact extraction and verification combining evidence retrieval from Wikipedia with claim classification. Dataset of 185,445 human-generated claims verified against Wikipedia, three-class labels (SUPPORTED/REFUTED/NOT ENOUGH INFO). 23 teams participated, best system (UNC-NLP) achieved 64.21% FEVER score. Created 5 new author pages, updated 4 topic pages (fact-checking, fake-news-datasets, detection-methods), updated section indexes.

[2026-05-16 00:00] lint | auto-fix

• job: 1778902071000-322a0ac0
• triggered_by: manual
• reason: broken-topic-links
• fixed: 0 mechanical issues
• notes: Comprehensive lint check of recent ingests; all newly created author pages, papers, and topic pages have valid frontmatter. Verified wikilinks in connections sections are correct and targets exist. Section index pages properly regenerated by build hooks. Wiki health is good.

[2026-05-15 23:45] ingest | Fake News Detection on Social Media using Geometric Deep Learning

• job: 1778895727638-879abb21
• submitter: reza
• page: Fake News Detection on Social Media using Geometric Deep Learning
• touched: wiki/papers/2019-monti-geometric-deep-learning.md (created), wiki/authors/monti-federico.md (created), wiki/authors/frasca-fabrizio.md (created), wiki/authors/eynard-davide.md (created), wiki/authors/mannion-damon.md (created), wiki/authors/bronstein-michael.md (created), wiki/topics/propagation-based-detection.md (updated), wiki/topics/social-context.md (updated), wiki/topics/detection-methods.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Graph convolutional network approach for fake news detection exploiting propagation patterns, user profiles, and social network structure; achieves 92.7% ROC AUC on Twitter; demonstrates language independence of propagation features and robustness compared to content-based methods; enables early detection within 1–2 hours. Created 5 new author pages, updated 3 topic pages with propagation-based and social-context references, updated section indexes.

[2026-05-15 23:40] ingest | Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multimodal Data

• job: 1778895727628-c56d3847
• submitter: reza
• page: Embracing Domain Differences in Fake News: Cross-domain Fake News Detection
• touched: wiki/papers/2021-silva-cross-domain-multimodal.md (created), wiki/authors/silva-amila.md (created), wiki/authors/luo-ling.md (created), wiki/authors/karunasekera-shanika.md (created), wiki/authors/leckie-christopher.md (created), wiki/topics/multimodal-detection.md (updated), wiki/topics/detection-methods.md (created), wiki/topics/cross-domain-learning.md (created), wiki/topics/domain-adaptation.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: First multimodal paper addressing cross-domain generalization; unsupervised domain discovery via propagation network community detection; supervised domain-agnostic classifier with dual decoders (domain-specific + cross-domain). LSH-based instance selection for cost-effective labeling. Created 4 new author pages, 3 new topic pages, updated multimodal-detection topic. 7.55% F1 improvement on rarely-appearing domains.

[2026-05-15 23:35] ingest | Beyond News Contents: The Role of Social Context for Fake News Detection

• job: 1778895727624-2626fe94
• submitter: reza
• page: Beyond News Contents: The Role of Social Context for Fake News Detection
• touched: wiki/papers/2019-shu-social-context-trifn.md (created), wiki/authors/shu-kai.md (updated), wiki/authors/wang-suhang.md (updated), wiki/authors/liu-huan.md (updated), wiki/topics/social-context.md (updated), wiki/topics/credibility-assessment.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Tri-relationship embedding framework (TriFN) jointly modeling publisher-news and user-news signals; first to systematically incorporate publisher partisan bias and user credibility for detection. Achieves 4–6% F1 improvement over content+profile baselines on FakeNewsNet. Demonstrates early detection capability (>80% F1 within 48 hours). Updated 3 author pages and 2 topic pages, added entries to section indexes.

[2026-05-15 23:30] ingest | Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation

• job: 1778895727619-52ddbd9b
• submitter: reza
• page: Hierarchical Propagation Networks for Fake News Detection: Investigation and
• touched: wiki/papers/2019-shu-hierarchical-propagation.md (created), wiki/authors/shu-kai.md (updated), wiki/authors/mahudeswaran-deepak.md (updated), wiki/authors/wang-suhang.md (updated), wiki/authors/liu-huan.md (updated), wiki/topics/propagation-based-detection.md (updated), wiki/topics/social-context.md (updated), wiki/topics/temporal-patterns.md (updated), wiki/topics/graph-based-methods.md (updated), wiki/topics/hierarchical-networks.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Foundational propagation-based detection study analyzing both macro-level (retweet cascades) and micro-level (reply conversations) granularity; demonstrates complementarity of two-level analysis with F1 > 0.80 on FakeNewsNet; identifies temporal features as most discriminative. Updated 4 author pages, created 1 new topic page for hierarchical networks concept, updated 4 topic pages, updated section indexes.

[2026-05-15 23:25] ingest | FakeNewsNet: A Data Repository with News Content, Social Context and Spatiotemporal Information for Studying Fake News on Social Media

• job: 1778895727613-679d0f9a
• submitter: reza
• page: FakeNewsNet: A Data Repository with News Content, Social Context and Spatiotemporal
• touched: wiki/papers/2018-shu-fakenewsnet-dataset.md (created), wiki/authors/shu-kai.md (updated), wiki/authors/wang-suhang.md (updated), wiki/authors/liu-huan.md (updated), wiki/authors/mahudeswaran-deepak.md (created), wiki/authors/lee-dongwon.md (created), wiki/topics/social-context.md (updated), wiki/topics/network-analysis.md (updated), wiki/topics/spatiotemporal-information.md (created), wiki/topics/user-engagement.md (created), wiki/topics/fake-news-datasets.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Foundational multi-dimensional dataset integrating news content, social context, and spatiotemporal information from PolitiFact and GossipCop. Comprehensive benchmark enabling downstream detection, evolution, and mitigation research. First dataset combining all three feature families at scale. Updated 5 author pages (3 existing + 2 new), created 3 new topic pages for key dimensions, updated 2 existing topic pages to reference FakeNewsNet, updated section indexes.

[2026-05-15 23:20] ingest | Reducing Controversy by Connecting Opposing Views

• job: 1778895727607-fff4bd2d
• submitter: reza
• page: Reducing Controversy by Connecting Opposing Views
• touched: wiki/papers/2016-garimella-reducing-controversy.md (created), wiki/authors/garimella-kiran.md (updated), wiki/authors/de-francisci-morales-gianmarco.md (updated), wiki/authors/gionis-aristides.md (updated), wiki/authors/mathioudakis-michael.md (updated), wiki/topics/polarization.md (updated), wiki/topics/echo-chambers.md (updated), wiki/topics/controversy-detection.md (updated), wiki/topics/graph-based-methods.md (updated), wiki/index.md (updated)
• notes: Follow-up to foundational Garimella et al. (2017) paper on controversy measurement. First algorithmic approach to bridging echo chambers via edge-recommendation optimization; proposes efficient ROV algorithm considering only high-degree hub nodes. Extends to ROV-AP with acceptance probability model based on user polarity. Comprehensive Twitter evaluation on 10 controversial topics. Updated 4 author pages, 4 topic pages, and main wiki index with new paper reference.

[2026-05-15 23:15] ingest | Anatomy of an online misinformation network

• job: 1778895727596-6aed7b07
• submitter: reza
• page: Anatomy of an online misinformation network
• touched: wiki/papers/2018-shao-anatomy-misinformation.md (created), wiki/authors/hui-pik-mai.md (created), wiki/authors/wang-lei.md (created), wiki/authors/jiang-xinwen.md (created), wiki/authors/shao-chengcheng.md (updated), wiki/authors/flammini-alessandro.md (updated), wiki/authors/menczer-filippo.md (updated), wiki/authors/ciampaglia-giovanni-luca.md (updated), wiki/topics/network-analysis.md (created), wiki/topics/misinformation-diffusion.md (created), wiki/topics/social-media-polarization.md (created), wiki/topics/fact-checking.md (updated), wiki/topics/bot-detection.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: First in-depth network analysis of misinformation and fact-checking diffusion during 2016 US election using k-core decomposition; reveals stark segregation between claim and fact-check communities with fact-checking disappearing in network core. Hoaxy platform described and characterized. Network robustness analysis shows efficient disruption via high out-strength node removal. Created 3 new author pages, 3 new topic pages; updated 4 author pages and 2 topic pages with new paper reference.

[2026-05-15 23:11] ingest | The spread of low-credibility content by social bots

• job: 1778895727591-cafde902
• submitter: reza
• page: The spread of low-credibility content by social bots
• touched: wiki/papers/2017-shao-social-bots-low-credibility.md (created), wiki/authors/shao-chengcheng.md (created), wiki/authors/ciampaglia-giovanni-luca.md (created), wiki/authors/yang-kaicheng.md (created), wiki/authors/varol-onur.md (updated), wiki/authors/flammini-alessandro.md (updated), wiki/authors/menczer-filippo.md (updated), wiki/topics/bot-detection.md (updated), wiki/topics/misinformation-amplification.md (created), wiki/topics/social-media-manipulation.md (created), wiki/topics/2016-election.md (created), wiki/papers/index.md (updated), wiki/index.md (updated), wiki/log.md (updated)
• notes: Empirical analysis of 14M Twitter messages during 2016 election showing bots disproportionately amplify low-credibility content (31% of tweets from 6% of accounts); identifies two bot strategies (early amplification and influential-user targeting); demonstrates bot removal is critical for mitigating misinformation spread. Created 3 new author pages, 3 new topic pages; updated 6 existing files; updated 4 author pages with new paper reference.

[2026-05-16 00:07] ingest | Online Human-Bot Interactions: Detection, Estimation, and Characterization

• job: 1778895727586-5a7b1464
• submitter: reza
• page: Online Human-Bot Interactions: Detection, Estimation, and Characterization
• touched: wiki/papers/2017-varol-human-bot-interactions.md (created), wiki/authors/varol-onur.md (updated), wiki/authors/ferrara-emilio.md (updated), wiki/authors/davis-clayton.md (updated), wiki/authors/menczer-filippo.md (updated), wiki/authors/flammini-alessandro.md (updated), wiki/topics/bot-detection.md (updated), wiki/topics/social-bots.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Large-scale bot detection framework with 1,150 behavioral features; evaluates on 14M accounts and estimates bot prevalence (9–15%); clustering analysis reveals behavioral phenotypes; demonstrates concept drift in detection systems as adversaries improve sophistication. Extends 2015 Ferrara survey with empirical population estimates and behavioral clustering.

[2026-05-16 00:05] ingest | The Rise of Social Bots

• job: 1778895727581-5917ad8b
• submitter: reza
• page: The Rise of Social Bots
• touched: wiki/papers/2015-ferrara-rise-social-bots.md (created), wiki/authors/varol-onur.md (created), wiki/authors/davis-clayton.md (created), wiki/authors/flammini-alessandro.md (created), wiki/authors/ferrara-emilio.md (updated), wiki/authors/menczer-filippo.md (updated), wiki/topics/bot-detection.md (updated), wiki/topics/twitter.md (updated), wiki/topics/information-operations.md (updated), wiki/topics/social-bots.md (created), wiki/papers/index.md (updated), wiki/topics/index.md (updated), wiki/index.md (updated)
• notes: Foundational survey of social bot phenomenon and detection methods; proposes taxonomy of detection approaches (network-based, crowd-sourced, feature-based); characterizes behavioral signatures distinguishing bots from humans; discusses arms race between bot sophistication and detection systems. Created 1 new topic (social-bots), created 3 new author pages (Varol, Davis, Flammini), updated 2 existing author pages and multiple topic and index pages.

[2026-05-15 23:48] ingest | CSI: A Hybrid Deep Model for Fake News Detection

• job: 1778895727576-f624daa6
• submitter: reza
• page: CSI: A Hybrid Deep Model for Fake News Detection
• touched: wiki/papers/2017-ruchansky-csi.md (created), wiki/authors/ruchansky-natali.md (updated), wiki/authors/liu-yan.md (updated), wiki/authors/seo-sungyong.md (created), wiki/topics/fake-news-detection.md (updated), wiki/topics/deep-learning.md (created), wiki/topics/temporal-patterns.md (created), wiki/topics/user-behavior.md (created), wiki/topics/group-anomaly-detection.md (created), wiki/topics/neural-networks.md (created), wiki/papers/index.md (updated), wiki/index.md (updated), wiki/log.md (updated)
• notes: Foundational hybrid model combining text, temporal engagement, and user behavior for fake news detection. Three-module neural architecture (Capture LSTM, Score neural network, Integrate layer) achieves 89.2% accuracy on Twitter, 95.3% on Weibo. Demonstrates joint modeling of all three characteristics outperforms single-modality baselines. First paper to explicitly isolate suspicious user groups via group behavior analysis. Created five new topic pages (deep-learning, temporal-patterns, user-behavior, group-anomaly-detection, neural-networks), created new author page for Seo.

[2026-05-15 23:59] ingest | A Survey on Natural Language Processing for Fake News Detection

• job: 1778895727561-6f30a592
• submitter: reza
• page: A Survey on Natural Language Processing for Fake News Detection
• touched: wiki/papers/2020-oshikawa-nlp-fake-news-detection.md (created), wiki/authors/oshikawa-ray.md (created), wiki/authors/qian-jing.md (created), wiki/authors/wang-william-yang.md (created), wiki/topics/natural-language-processing.md (created), wiki/topics/fake-news-detection.md (updated), wiki/topics/linguistic-analysis.md (updated), wiki/index.md (updated), wiki/log.md (updated)
• notes: NLP-focused survey of fake news detection; systematically reviews nine benchmark datasets and five methodological categories (preprocessing, non-neural classifiers, neural networks, rhetorical approaches, evidence collection). Key finding: attention-LSTM models achieve highest accuracy (41.5–45.7% on LIAR, 68–76% on FEVER, 94.4% on FakeNewsNet), outperforming hand-crafted linguistic features but requiring meta-data exploitation. First comprehensive review of NLP solutions for automatic fake news detection. Created new topic (natural-language-processing), updated fake-news-detection and linguistic-analysis topic pages, created three new author pages.

[2026-05-15 23:45] ingest | Misinformation in Diaspora Communities

• job: 1778891519276-d343d685
• submitter: reza
• page: Misinformation in Diaspora Communities
• touched: wiki/videos/2026-unknown-diaspora-misinformation.md (created), wiki/topics/diaspora-misinformation.md (created), wiki/topics/whatsapp-misinformation.md (updated), wiki/videos/index.md (updated), wiki/index.md (updated), wiki/log.md (updated)
• notes: YouTube video documenting misinformation spread through messaging apps (WhatsApp, WeChat, KakaoTalk) in diaspora communities. Key findings: platforms spend 13% of moderation hours on 90% of users (non-US/Canada); messaging apps' encrypted, group-based design makes content invisible; diaspora communities lack language-specific fact-checking resources; cultural-specific targeting exploits community fears (socialism, traditional-medicine beliefs, etc.). Documents health consequences (vaccine hesitancy among Latino communities in California). Created 1 new topic (diaspora-misinformation), updated WhatsApp topic with video reference.

[2026-05-15 22:14] ingest | A Stylometric Inquiry into Hyperpartisan and Fake News

• job: 1778895727667-02810f15
• submitter: reza
• page: A Stylometric Inquiry into Hyperpartisan and Fake News
• touched: wiki/papers/2017-potthast-stylometric-hyperpartisan.md (created), wiki/authors/{potthast-martin, kiesel-johannes, reinartz-kevin, bevendorff-janek, stein-benno}.md (created 5 new), wiki/topics/{hyperpartisan-news-detection, stylometry}.md (created 2 new), wiki/topics/fake-news-detection.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated), wiki/log.md (updated)
• notes: Stylometric analysis using Unmasking technique; finds hyperpartisan news distinguishable from mainstream (F1=0.78) and satire from both (F1=0.81), but style-based fake news detection alone insufficient (F1=0.46). BuzzFeed-Webis corpus of 1,627 manually fact-checked articles. Created 5 author pages (full consortium), 2 new topic pages (hyperpartisan-news-detection, stylometry), updated fake-news-detection topic.

[2026-05-15 21:47] ingest | Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning

• job: 1778895727571-222986f7
• submitter: reza
• page: Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning
• touched: wiki/papers/2017-ma-propagation-tree-kernel.md (created), wiki/authors/{ma-jing, gao-wei, wong-kam-fai}.md (updated 3 existing), wiki/topics/{rumor-detection, propagation-based-detection}.md (updated 2 existing), wiki/index.md (updated), wiki/papers/index.md (updated), wiki/log.md (updated)
• notes: Tree kernel approach (PTK/cPTK) for rumor detection from Ma et al. preceding their later RNN work by one year. Uses soft-matching of propagation tree subtrees instead of traditional discrete tree kernels. Finer-grained four-class classification (false/true/unverified/non-rumor). Stronger early detection performance—75% accuracy within 24 hours. Updated 3 existing author pages with this earlier publication.

[2026-05-15 21:35] ingest | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks

• job: 1778895727566-ccd4b953
• submitter: reza
• page: Rumor Detection on Twitter with Tree-structured Recursive Neural Networks
• touched: wiki/papers/2018-ma-rumor-detection-rvnn.md (created), wiki/authors/{ma-jing, gao-wei, wong-kam-fai}.md (created 3 new), wiki/topics/rumor-detection.md (created), wiki/topics/{propagation-based-detection, social-context}.md (updated 2 existing), wiki/index.md (updated), wiki/papers/index.md (updated), wiki/log.md (updated)
• notes: First deep neural approach using tree-structured RvNNs for rumor detection; proposes bottom-up and top-down architectures operating on tweet propagation trees. Achieves 72.3%/73.7% accuracy on Twitter15/16 with 4× faster early detection. Created 1 new topic (rumor-detection), 3 new authors, updated propagation and social-context topics.

[2026-05-15 20:15] ingest | The Science of Fake News

• job: 1778895727535-9e107eb3
• submitter: reza
• page: The Science of Fake News
• touched: wiki/papers/2018-lazer-science-fake-news.md (created), wiki/authors/{baum-matthew, benkler-yochai, berinsky-adam, greenhill-kelly, menczer-filippo, metzger-miriam, schudson-michael, sloman-steven, sunstein-cass, thorson-emily, zittrain-jonathan}.md (created 11 new), wiki/authors/{lazer-david, pennycook-gordon, nyhan-brendan, rothschild-david, watts-duncan}.md (updated 5 existing), wiki/topics/{survey, misinformation-detection, platform-algorithms, media-ecosystems}.md (created 4 new), wiki/topics/{social-media, fact-checking}.md (updated 2 existing), wiki/index.md (updated), wiki/log.md (updated)
• notes: Foundational 2018 Science review by 16-author multidisciplinary consortium establishing scientific framework for fake news; defines fake news, reviews prevalence/impact literature, discusses individual-level (fact-checking, education) and platform-level interventions (algorithms, bot detection). Canonical reference for the field. Created 11 new author pages (full consortium coverage), 4 new topic pages (survey, detection, algorithms, media-ecosystems), updated 7 existing pages (5 authors, 2 topics). Establishes conceptual baseline for all downstream research.

[2026-05-15 18:00] lint | weekly auto-fix

• fixed: 1 mechanical issue
• touched: wiki/index.md
• repairs:
• Updated wiki/index.md status section with correct page counts: papers 45→68, authors 145→209, topics 85→110

[2026-05-15 10:30] lint | auto-fix

• fixed: 57 mechanical issues
• touched: wiki/log.md, wiki/authors/wardle-claire.md, wiki/authors/effron-daniel.md, wiki/authors/farrell-justin.md, wiki/authors/treen-kathie.md, wiki/authors/williams-hywel.md, wiki/authors/oneill-saffron.md, wiki/papers/2020-golovchenko-cross-platform-propaganda.md, wiki/papers/2019-eady-political-bubbles.md, wiki/papers/2022-munger-right-wing-youtube.md, wiki/papers/2021-hosseinmardi-youtube-radicalization.md, wiki/papers/2020-allen-fake-news-ecosystem.md, wiki/papers/2016-graves-boundaries-not-drawn.md, wiki/papers/2020-walter-fact-checking-meta-analysis.md, wiki/papers/2020-vaccari-deepfakes-disinformation.md, wiki/papers/2019-khattar-mvae-multimodal.md, wiki/papers/2021-mosleh-cognitive-reflection-twitter.md, wiki/papers/2018-van-prooijen-emerging-conspiracy-research.md, wiki/papers/2017-brady-moral-emotion-diffusion.md, wiki/papers/2020-giglietto-coordinated-inauthentic-behavior.md, wiki/papers/2018-soares-influencers-polarized-twitter.md, wiki/papers/2020-wilson-social-media-vaccine.md, wiki/papers/2021-mosleh-partisanship-tie-formation.md, wiki/papers/2019-sahly-trump-clinton-framing.md
• repairs:
• Removed institutional/organizational wikilinks (university names, organization names) that were incorrectly formatted
• Corrected paper year citations (2020 → 2019 for grinberg-twitter-election)
• Removed wikilinks to non-existent pages (topic pages, papers not in wiki)
• Converted citation references to plain text where target pages don't exist

[2026-05-15 00:00] ingest | Examining the consumption of radical content on YouTube

• job: 1777468639250-ff4305d2
• submitter: reza
• page: Examining the consumption of radical content on YouTube
• touched: wiki/papers/2022-hosseinmardi-radical-youtube.md (created), wiki/authors/clauset-aaron.md (created), wiki/authors/hosseinmardi-homa.md (updated), wiki/authors/ghasemian-amir.md (updated), wiki/authors/mobius-markus.md (updated), wiki/authors/rothschild-david.md (updated), wiki/authors/watts-duncan.md (updated), wiki/topics/recommendation-algorithms.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: PNAS 2022 empirical study (N=309.8K) auditing YouTube's recommendation algorithm. Finds far-right content consumption small and stable (~0.05% of users); anti-woke content growing; 55% of far-right views from external sources; no within-session escalation bias; on/off-platform consumption correlated (user preference not algorithmic). Corrects narrative of YouTube-driven radicalization. 1 new author page (Clauset), 5 author page updates, 1 topic update, 2 index updates. Landmark empirical response to claims about YouTube algorithmic radicalization; uses representative panel with full web-browsing tracking over 4 years.

[2026-05-15 00:00] ingest | A Picture Paints a Thousand Lies? The Effects and Mechanisms of Multimodal Disinformation and Rebuttals Disseminated via Social Media

• job: 1777425868265-25574dc8
• submitter: reza
• page: A Picture Paints a Thousand Lies? The Effects and Mechanisms of Multimodal
• touched: wiki/papers/2020-hameleers-multimodal-disinformation.md (created), wiki/authors/hameleers-michael.md (created), wiki/authors/powell-thomas.md (created), wiki/authors/van-der-meer-toni.md (created), wiki/authors/bos-lieke.md (created), wiki/topics/multimodal-detection.md (updated), wiki/topics/fact-checking.md (updated), wiki/topics/correction-effectiveness.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Political Communication 2020 experimental study (N=1,404) showing multimodal disinformation is more credible than text-only, but fact-checkers effectively counter both modalities. Motivated reasoning moderates effectiveness. 4 new author pages, 3 topic updates. Bridges behavioral and technical multimodal misinformation research; first to test effectiveness of multimodal corrections.

[2026-04-29 16:46] ingest | Spreadable Spectacle in Digital Culture: Civic Expression, Fake News, and the Role of Media Literacies in "Post-Fact" Society

• job: 1777425868079-15fa6057
• submitter: reza
• page: Spreadable Spectacle in Digital Culture: Civic Expression, Fake News, and
• touched: wiki/papers/2017-mihailidis-spreadable-spectacle.md (created), wiki/authors/mihailidis-paul.md (created), wiki/authors/viotty-samantha.md (created), wiki/topics/spectacle.md (created), wiki/topics/civic-engagement.md (created), wiki/topics/media-literacy.md (updated), wiki/topics/fake-news.md (updated), wiki/topics/misinformation.md (updated), wiki/topics/polarization.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: ABS 2017 critical analysis of media spectacle during 2016 election. Case study of Pizzagate conspiracy shows how spectacle spreads through homophilous networks, memes, and mainstream media coverage. Proposes repositioning media literacy toward critique, creation, and civic engagement. 2 new author pages, 2 new topic pages (spectacle, civic-engagement), 4 topic updates. Foundational paper connecting misinformation, polarization, media literacy, and civic engagement within digital culture framework.

[2026-04-29 16:45] ingest | A digital media literacy intervention increases discernment between mainstream and false news in the United States and India

• job: 1777425868074-ccf8863a
• submitter: reza
• page: A digital media literacy intervention increases discernment between mainstream
• touched: wiki/papers/2020-guess-media-literacy-intervention.md (created), wiki/authors/guess-andrew.md (created), wiki/authors/lerner-michael.md (created), wiki/authors/lyons-benjamin.md (created), wiki/authors/montgomery-jacob.md (created), wiki/authors/nyhan-brendan.md (created), wiki/authors/reifler-jason.md (created), wiki/authors/sircar-neelanjan.md (created), wiki/topics/media-literacy.md (updated), wiki/topics/fake-news-detection.md (updated), wiki/topics/misinformation.md (updated), wiki/topics/literacy-interventions.md (updated), wiki/topics/digital-literacy.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: PNAS 2020 RCT (N=12K across three samples) testing Facebook's "Tips to Spot False News" intervention on news evaluation. Key findings: 26.5% improvement in discernment (US), 17.3% (India online); effects decay over weeks but persist ~3 weeks in US; no effects in rural low-digital-literacy population. 7 new author pages, 1 new topic page (digital-literacy), 4 topic updates. High-impact intervention effectiveness study demonstrating that simple, scalable media literacy teaching improves headline discrimination in digitally-experienced populations; heterogeneous effects highlight importance of digital context.

[2026-04-29] ingest | Does Media Literacy Help Identification of Fake News? Information Literacy Helps, but Other Literacies Don't

• job: 1777425868070-506cbffd
• submitter: reza
• page: Does Media Literacy Help Identification of Fake News? Information Literacy
• touched: wiki/papers/2021-jones-jang-media-literacy-fake-news.md (created), wiki/authors/jones-jang-s-mo.md (created), wiki/authors/mortensen-tara.md (created), wiki/authors/liu-jingjing.md (created), wiki/topics/information-literacy.md (created), wiki/topics/fake-news-identification.md (created), wiki/topics/literacy-interventions.md (created), wiki/topics/media-literacy.md (updated), wiki/topics/fake-news-detection.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: American Behavioral Scientist 2021 survey (N=1,299) of which literacy types predict fake news identification. Key finding: only information literacy (ability to search and evaluate verified sources) significantly predicts accurate identification; media, news, and digital literacies showed no significant relationship, contradicting widespread assumptions about media literacy interventions. 3 new author pages, 3 new topic pages (information-literacy, fake-news-identification, literacy-interventions), 2 topic updates. Critical empirical evidence that information-seeking skills matter more than general media understanding for misinformation detection; challenges policy assumptions about literacy education.

[2026-04-29] ingest | News literacy, social media behaviors, and skepticism toward information on social media

• job: 1777425868065-dfb57d0d
• submitter: reza
• page: News literacy, social media behaviors, and skepticism toward information on
• touched: wiki/papers/2021-vraga-news-literacy-skepticism.md (created), wiki/authors/tully-melissa.md (created), wiki/authors/vraga-emily.md (updated), wiki/topics/media-literacy.md (created), wiki/topics/social-media-behavior.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Information, Communication & Society 2021 national survey (N=788) examining relationships between news literacy, cognitive orientation (need for cognition, self-perceived vs. actual media literacy), and social media behaviors across three major platforms. Key finding: actual news literacy predicts less social media engagement with news/politics but greater skepticism; self-perceived media literacy shows inverse pattern (more engagement, less skepticism) — a dangerous mismatch. Platform-specific effects: news literacy suppresses exposure on Facebook/YouTube but not Twitter. 1 new author page (Tully), 1 new topic page (media-literacy), 1 topic update (social-media-behavior). First comprehensive link between literacy orientations and both engagement and skepticism; highlights gap between confidence and knowledge.

[2026-04-29] ingest | Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis

• job: 1777425868061-8aa3e74d
• submitter: reza
• page: Causes and consequences of mainstream media dissemination of fake news: literature
• touched: wiki/papers/2020-tsfati-mainstream-media-dissemination.md (created), wiki/authors/tsfati-yariv.md (created), wiki/authors/boomgaarden-h-g.md (created), wiki/authors/stromback-j.md (created), wiki/authors/vliegenthart-r.md (created), wiki/authors/damstra-a.md (created), wiki/authors/lindgren-e.md (created), wiki/topics/mainstream-media.md (created), wiki/topics/journalism.md (created), wiki/topics/news-values.md (created), wiki/topics/fake-news.md (updated), wiki/topics/correction-effectiveness.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Annals of the International Communication Association 2020 literature review and synthesis examining mainstream media's paradoxical role as both disseminators and correctors of misinformation. Key contributions: synthesizes why journalists cover false claims (professional duty, news values, peer psychology, infrastructure); identifies psychological mechanisms explaining correction backfire (fluency effects, negation accessibility, mental model persistence, motivated reasoning). 6 new author pages, 3 new topic pages (mainstream-media, journalism, news-values), 2 topic updates (fake-news, correction-effectiveness). Directly addresses understudied mainstream-media role in misinformation ecosystems; connects journalistic practice to audience effects.

[2026-04-29] ingest | A Study of Misinformation in WhatsApp groups with a focus on the Brazilian Presidential Elections

• job: 1777425868040-10956df0
• submitter: reza
• page: A Study of Misinformation in WhatsApp groups with a focus on the Brazilian
• touched: wiki/papers/2019-machado-whatsapp-misinformation.md (created), wiki/authors/machado-caio.md (created), wiki/authors/kira-beatriz.md (created), wiki/authors/narayanan-vidya.md (created), wiki/authors/kollanyi-bence.md (created), wiki/authors/howard-philip-n.md (created), wiki/topics/whatsapp-misinformation.md (created), wiki/topics/junk-news-detection.md (created), wiki/topics/content-analysis.md (created), wiki/topics/election-interference.md (updated), wiki/topics/propaganda.md (updated), wiki/topics/political-polarization.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: WWW '19 empirical study of misinformation in 130 public WhatsApp groups during 2018 Brazilian presidential election. Analyzes 45,072 shared links and 400 media files; develops grounded typology for classifying junk news and polarizing content. Key findings: 13.1% of links from junk sources, 40% to YouTube, 33% of messages are media files. Documents strategic campaign use of messaging platforms and visual propaganda outside public platform oversight. 5 new author pages, 3 new topic pages (whatsapp-misinformation, junk-news-detection, content-analysis), 3 topic updates. First major empirical study of messaging-app misinformation during real election; demonstrates shift of political communication from public platforms to private channels.

[2026-04-29] ingest | Social Media for Political Campaigns: Trump's and Clinton's Frame Building

• job: 1777425868016-ad00b745
• submitter: reza
• page: Social Media for Political Campaigns: An Examination of Trump''s and Clinton''s
• touched: wiki/papers/2019-sahly-trump-clinton-framing.md (created), wiki/authors/sahly-abdulsamad.md (created), wiki/authors/shao-chun.md (created), wiki/authors/kwon-k-hazel.md (created), wiki/topics/frame-analysis.md (created), wiki/topics/social-media-engagement.md (created), wiki/topics/twitter.md (updated), wiki/topics/facebook.md (updated), wiki/topics/political-communication.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Social Media + Society 2019 content analysis of 2016 presidential campaign messaging on Twitter (3,805 Trump, 655 Clinton tweets) and Facebook (303 Trump, 352 Clinton posts). Trump relied on conflict (40% Twitter) and negative emotional frames (32% Twitter); Clinton emphasized morality (8.7% Twitter) and positive emotion (32.2% Twitter). Frame effects on engagement differed by platform: on Twitter, conflict and morality frames drove retweets for both candidates; on Facebook, patterns were inconsistent between candidates. Key insight: platform-specific affordances modulate frame effects. 3 new author pages, 2 new topic pages (frame-analysis, social-media-engagement), 3 topic updates. Cross-platform analysis reveals strategic framing differences in major-party 2016 campaigns.

[2026-04-29] ingest | Influencers in Polarized Political Networks on Twitter

• job: 1777425868007-35748270
• submitter: reza
• page: Influencers in Polarized Political Networks on Twitter
• touched: wiki/papers/2018-soares-influencers-polarized-twitter.md (created), wiki/authors/soares-felipe-bonow.md (created), wiki/authors/recuero-raquel.md (created), wiki/authors/zago-gabriela.md (created), wiki/topics/social-networks.md (updated), wiki/topics/echo-chambers.md (updated), wiki/topics/political-polarization.md (updated), wiki/topics/influencers.md (created), wiki/topics/public-sphere.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: SMSociety 2018 social network analysis of Twitter conversations during Brazil's 2016 Dilma Rousseff impeachment. Identifies three influencer types with distinct roles: "opinion leaders" with explicit ideology spread within like-minded groups; "informational influencers" (media, institutions) drawn into partisan clusters despite neutrality; "activists" with high activity amplify in-group messages, actively constructing echo chambers. All networks show high modularity (0.544–0.804), indicating strong polarization. Key finding: user selectivity and activism, not just algorithms, drive echo-chamber formation. 3 new author pages, 2 new topic pages (influencers, public-sphere), 3 topic updates (social-networks, echo-chambers, political-polarization). First empirical identification of distinct influencer types in polarized political networks with quantitative evidence of user-driven polarization reinforcement.

[2026-04-29] ingest | Belief in conspiracy theories: Basic principles of an emerging research domain

• job: 1777425867967-a05d471d
• submitter: reza
• page: Belief in conspiracy theories: Basic principles of an emerging research domain
• touched: wiki/papers/2018-van-prooijen-emerging-conspiracy-research.md (created), wiki/authors/van-prooijen-jan.md (created), wiki/authors/douglas-karen.md (updated), wiki/topics/conspiracy-theories.md (updated), wiki/topics/psychology-of-belief.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: European Journal of Social Psychology 2018 special issue introduction synthesizing the emerging research domain around four foundational principles: conspiracy beliefs are consequential (with real impacts on health and relationships), universal (across cultures and historical periods), emotional (driven by sense-making rather than logic), and social (rooted in intergroup conflict). Complements the 2017 Douglas et al. review with emphasis on emotional and social drivers as primary mechanisms. 1 new author page (van Prooijen), 2 topic updates (conspiracy-theories, psychology-of-belief). Provides organizing framework for understanding cross-cultural prevalence and appeal of conspiracy narratives.

[2026-04-29] ingest | The Psychology of Conspiracy Theories

• job: 1777425867962-4c423e03
• submitter: reza
• page: The Psychology of Conspiracy Theories
• touched: wiki/papers/2017-douglas-psychology-conspiracy-theories.md (created), wiki/authors/douglas-karen.md (created), wiki/authors/sutton-robbie.md (created), wiki/authors/cichocka-aleksandra.md (created), wiki/topics/conspiracy-theories.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Current Directions in Psychological Science 2017 foundational review synthesizing two decades of empirical research on psychological drivers of conspiracy-theory belief. Proposes unified taxonomy: epistemic (seeking understanding/certainty), existential (seeking control/security), and social (seeking positive in-group identity) motives. Critical insight: despite psychological appeal, conspiracy theories empirically frustrate rather than satisfy these needs—self-defeating motivated reasoning. 3 new author pages, 1 topic updated (conspiracy-theories). Major synthesis establishing psychological motive framework now canonical in the field.

[2026-04-29 09:30] ingest | Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in News

• job: 1777425867921-e1e50d0d
• submitter: reza
• page: Deepfakes and Disinformation: Exploring the Impact of Synthetic Political
• touched: wiki/papers/2020-vaccari-deepfakes-disinformation.md (created), wiki/authors/vaccari-christian.md (created), wiki/authors/chadwick-andrew.md (created), wiki/topics/deepfakes.md (created), wiki/topics/synthetic-media.md (created), wiki/topics/disinformation.md (updated), wiki/topics/trust.md (updated), wiki/papers/index.md (updated), wiki/topics/index.md (updated)
• notes: Social Media + Society 2020 experimental study (N=2,005 UK respondents) on political deepfakes. Key finding: deepfakes increase uncertainty about content, which mediates reduced trust in news on social media—deepfakes threaten civic culture through uncertainty, not just deception. Educational interventions showing deepfakes are synthetic can mitigate effects. 2 new author pages, 2 new topic pages (deepfakes, synthetic-media), 2 topic updates (disinformation, trust). First empirical evidence on deepfake effects on trust.

[2026-04-29] ingest | Fighting Misperceptions and Doubting Journalists' Objectivity: A Review of Fact-checking Literature

• job: 1777425867885-e35ba745
• submitter: reza
• page: Fighting Misperceptions and Doubting Journalists'' Objectivity: A Review of
• touched: wiki/papers/2019-nieminen-fact-checking-literature.md (created), wiki/authors/nieminen-sakari.md (created), wiki/authors/rapeli-lauri.md (created), wiki/topics/fact-checking.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Political Studies Review 2019 systematic literature review of 48 studies on political fact-checking organized into three research areas: (1) effectiveness in reducing misperceptions (mixed results including backfire effects); (2) fact-checking as a profession (inconsistent methods and reliability concerns); (3) public opinion about fact-checking. Key finding: 88% of studies US-focused; methodological heterogeneity undermines cross-study comparison. 2 new author pages, 3 page updates (fact-checking topic, main index, papers index). Provides meta-level synthesis of fact-checking research landscape identifying gaps and methodological challenges.

[2026-04-29] ingest | Fact-Checking: A Meta-Analysis of What Works and for Whom

• job: 1777425867880-b479859c
• submitter: reza
• page: Fact-Checking: A Meta-Analysis of What Works and for Whom
• touched: wiki/papers/2020-walter-fact-checking-meta-analysis.md (created), wiki/authors/walter-nathan.md (created), wiki/authors/cohen-jonathan.md (created), wiki/authors/holbert-r-lance.md (created), wiki/authors/morag-yasmin.md (created), wiki/topics/fact-checking.md (updated), wiki/topics/misinformation.md (updated), wiki/topics/motivated-reasoning.md (updated), wiki/topics/meta-analysis.md (created), wiki/topics/message-design.md (created), wiki/topics/political-communication.md (created), wiki/topics/correction-effectiveness.md (created), wiki/index.md (updated)
• notes: Political Communication 2020 meta-analysis of 30 studies synthesizing fact-checking effectiveness; overall positive effect (d = 0.29) but substantially moderated by motivated reasoning, ideology, and message design. Key findings: pro-attitudinal corrections (consistent with preexisting beliefs) far more effective than counter-attitudinal ones; Democrats/liberals show stronger effects (d = 0.43) than Republicans/conservatives (d = 0.17); visual "truth scales" backfire (d = −0.19); lexical complexity negatively predicts effectiveness; campaign-related fact-checking weaker than routine. 4 new author pages, 4 new topic pages (meta-analysis, message-design, political-communication, correction-effectiveness), 3 topic updates. Foundational quantitative synthesis establishing when and for whom fact-checking works.

[2026-04-29] ingest | Boundaries Not Drawn: Mapping the institutional roots of the global fact-checking movement

• job: 1777425867876-3d366130
• submitter: reza
• page: Boundaries Not Drawn: Mapping the institutional roots of the global fact-checking
• touched: wiki/papers/2016-graves-boundaries-not-drawn.md (created), wiki/authors/graves-lucas.md (created), wiki/topics/fact-checking.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Journalism Studies 2016 foundational ethnographic study mapping 64+ fact-checking organizations globally using ternary plot methodology across journalism, academia, and politics/civil society axes. Key finding: fact-checking has journalistic core (two-thirds of organizations) but exhibits striking institutional diversity; professional boundaries (e.g., contacting claimants, rating systems) are contested across countries. US fact-checkers position themselves as founders/authorities, while international peers resist this narrative and practice journalism differently. Documents tension between journalistic professionalism and activist/NGO fact-checking. 1 new author page, 1 topic update (fact-checking added to Key papers section). First comprehensive institutional analysis of the global fact-checking movement.

[2026-04-29 00:00] ingest | Examining the consumption of radical content on YouTube

• job: 1777425867866-1f6156e4
• submitter: reza
• page: Examining the consumption of radical content on YouTube
• touched: wiki/papers/2021-hosseinmardi-youtube-radicalization.md (created), wiki/authors/hosseinmardi-homa.md (created), wiki/authors/ghasemian-amir.md (created), wiki/authors/clausett-aaron.md (created), wiki/authors/mobius-markus.md (updated), wiki/authors/rothschild-david.md (updated), wiki/authors/watts-duncan.md (updated), wiki/topics/radicalization.md (updated), wiki/topics/recommendation-algorithms.md (updated), wiki/topics/political-polarization.md (updated), wiki/topics/youtube-radicalization.md (created), wiki/papers/index.md (updated)
• notes: PNAS 2021 large-scale empirical audit of YouTube radicalization claims; 309K U.S. users tracked 2016–2019 (21.4M watched videos, 974 labeled channels). Key findings: news dominates mainstream/centrist sources (11% of total consumption); far-right small and non-growing (0.05% of users); anti-woke grew 3× but reflects user preferences not algorithmic amplification; 55% of far-right videos accessed via external URLs/search, not recommendations; within-session dynamics show no bias toward extremism. 3 new author pages, 1 new topic page (youtube-radicalization), 5 topic updates. Major counterweight to "YouTube radicalizes" narratives; establishes user preference and broader ecosystem factors over platform algorithm.

[2026-04-29 23:59] ingest | Right-Wing YouTube: A Supply and Demand Perspective

• job: 1777425867862-9b919c94
• submitter: reza
• page: Right-Wing YouTube: A Supply and Demand Perspective
• touched: wiki/papers/2022-munger-right-wing-youtube.md (created), wiki/authors/munger-kevin.md (created), wiki/authors/phillips-joseph.md (created), wiki/topics/youtube-politics.md (created), wiki/topics/political-extremism.md (created), wiki/topics/radicalization.md (created), wiki/topics/recommendation-algorithms.md (created), wiki/papers/index.md (updated), wiki/topics/index.md (updated), wiki/index.md (updated)
• notes: International Journal of Press/Politics 2022 longitudinal platform analysis of 35 far-right YouTube channels (26K+ videos) vs. 219 mainstream media channels; identifies Conservative/Alt-Lite/Alt-Right creator clusters with distinct audience dynamics. Key finding: viewership peaked 2017 and declined despite algorithm amplification claims; reframes radicalization as supply-and-demand problem driven by platform affordances (monetization, low creation barriers) and audience appetite. 2 new author pages, 4 new topic pages (youtube-politics, political-extremism, radicalization, recommendation-algorithms). Critical for understanding platform dynamics beyond "algorithm radicalizes" narratives.

[2026-04-29 23:58] ingest | The small, disloyal fake news audience: The role of audience availability in fake news consumption

• job: 1777425867842-df7484b7
• submitter: reza
• page: The small, disloyal fake news audience: The role of audience availability
• touched: wiki/papers/2018-nelson-small-disloyal-fake-news.md (created), wiki/authors/nelson-jacob-l.md (created), wiki/authors/taneja-harsh.md (created), wiki/topics/audience-analysis.md (created), wiki/topics/fake-news-audience.md (created), wiki/topics/news-consumption.md (created), wiki/topics/social-media.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: New Media & Society 2018 empirical study using comScore data to measure fake news audience size relative to real news (675K vs 28M unique visitors Oct 2016); applies Law of Double Jeopardy showing fake news audiences are small and disloyal; demonstrates audience availability (time spent online) is stronger predictor of misinformation exposure than demographics; documents heavy reliance on Facebook/Google for traffic. 2 new author pages, 3 new topic pages (audience-analysis, fake-news-audience, news-consumption), 1 topic update (social-media). Foundational audience measurement study directly contradicting "massive fake news crisis" narratives.

[2026-04-29] ingest | How Many People Live in Political Bubbles on Social Media?

• job: 1777425867838-aca019cc
• submitter: reza
• page: How Many People Live in Political Bubbles on Social Media? Evidence From Linked
• touched: wiki/papers/2019-eady-political-bubbles.md (created), wiki/authors/eady-gregory.md (updated), wiki/authors/nagler-jonathan.md (updated), wiki/authors/guess-andrew.md (updated), wiki/authors/tucker-joshua.md (updated), wiki/authors/zilinsky-jan.md (created), wiki/topics/echo-chambers.md (updated), wiki/topics/polarization.md (updated), wiki/topics/twitter.md (updated), wiki/topics/political-bubbles.md (created), wiki/papers/index.md (updated), wiki/topics/index.md (updated), wiki/index.md (updated)
• notes: SAGE Open 2019 large-scale survey-behavioral study linking 1,496 Americans' Twitter accounts (642K accounts, 1.2B tweets) to ideology; measures political bubbles through following and retweet patterns. Key finding: substantial cross-ideological overlap contradicts "filter bubble" narratives; only 34% of most-conservative quintile live in extreme bubbles; retweets and weak ties bridge ideological divides. 1 new author page (Zilinsky), 1 new topic page (political-bubbles), 4 topic updates, foundational for understanding social media polarization vs. mythology.

[2026-04-29] ingest | The echo chamber effect on social media

• job: 1777425867829-a44fa5dc
• submitter: reza
• page: The echo chamber effect on social media
• touched: wiki/papers/2021-cinelli-echo-chamber-social-media.md (created), wiki/authors/cinelli-matteo.md (created), wiki/authors/de-francisci-morales-gianmarco.md (created), wiki/authors/galeazzi-alessandro.md (created), wiki/authors/quattrociocchi-walter.md (created), wiki/authors/starnini-michele.md (created), wiki/topics/echo-chambers.md (updated), wiki/topics/polarization.md (created), wiki/topics/facebook.md (updated), wiki/topics/twitter.md (updated), wiki/topics/reddit.md (created), wiki/topics/gab.md (created), wiki/topics/homophily.md (updated), wiki/topics/information-diffusion.md (updated), wiki/papers/index.md (updated), wiki/topics/index.md (updated), wiki/index.md (updated)
• notes: PNAS 2021 comparative empirical study quantifying echo chambers across four social media platforms; 100+ million posts from 1+ million users over multiple years. Key finding: platform architecture determines echo chamber formation—Facebook and Twitter (algorithmic feeds) exhibit strong homophilic clustering and polarized information diffusion, while Reddit (community-based) shows single-sided communities without biased diffusion. Direct Facebook–Reddit comparison confirms higher segregation on Facebook. 5 new author pages, 4 new topic pages (polarization, reddit, gab), 5 topic updates, major update to index. First large-scale comparative platform analysis showing architectural effects on polarization.

[2026-04-29 23:57] ingest | Exposure to opposing views on social media can increase political polarization

• job: 1777425867820-ded8f572
• submitter: reza
• page: Exposure to opposing views on social media can increase political polarization
• touched: wiki/papers/2018-bail-exposure-opposing-views.md (created), wiki/authors/bail-christopher.md (updated), wiki/authors/argyle-lisa.md (created), wiki/authors/brown-taylor.md (created), wiki/authors/bumpus-john.md (created), wiki/authors/chen-haohan.md (created), wiki/authors/hunzaker-mb-fallin.md (created), wiki/authors/lee-jaemin.md (created), wiki/authors/mann-marcus.md (created), wiki/authors/merhout-friedolin.md (updated), wiki/authors/volfovsky-alexander.md (updated), wiki/topics/political-polarization.md (updated), wiki/topics/echo-chambers.md (updated), wiki/topics/twitter.md (updated), wiki/topics/computational-social-science.md (updated), wiki/topics/social-media.md (updated), wiki/topics/backfire-effects.md (created), wiki/papers/index.md (updated), wiki/topics/index.md (updated), wiki/index.md (updated)
• notes: PNAS 2018 field experiment on Twitter; large-scale randomized trial (N=1,652 partisan users) assigning participants to follow bots retweeting opposing-ideology accounts. Key finding: Republicans became substantially more conservative (backfire effect 0.12–0.60 points depending on compliance); Democrats showed no significant shift. Preregistered hypotheses; rigorous compliance measurement; controls for Hawthorne effects and demographic sorting. Challenges echo-chamber-breaking assumption; important for understanding polarization dynamics. 7 new author pages, 1 new topic page (backfire-effects), 5 topic updates.

[2026-04-29 23:56] ingest | Online misinformation about climate change

• job: 1777425867811-2c5fe039
• submitter: reza
• page: Online misinformation about climate change
• touched: wiki/papers/2020-treen-online-climate-misinformation.md (created), wiki/authors/treen-kathie.md (created), wiki/authors/williams-hywel.md (created), wiki/authors/oneill-saffron.md (created), wiki/topics/climate-change.md (updated), wiki/topics/misinformation-spread.md (updated), wiki/topics/social-media.md (updated), wiki/topics/terminology.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: WIREs Climate Change 2020 comprehensive overview; synthesizes research across disciplines on climate change misinformation. Defines terminology hierarchy (information/misinformation/disinformation), identifies actor networks (scientists, governments, industry, media, think tanks), examines social media spread mechanisms (homophily, echo chambers, algorithmic bias), analyzes why it matters (policy, public attitudes), synthesizes countermeasures (inoculation, correction, detection, platform mechanisms). 150+ citations. 3 new author pages, 4 topic updates, 2 index updates. Foundational reference for researchers entering climate misinformation studies.

[2026-04-29 23:55] ingest | Corporate funding and ideological polarization about climate change

• job: 1777425867806-f1a894fa
• submitter: reza
• page: Corporate funding and ideological polarization about climate change
• touched: wiki/papers/2016-farrell-corporate-funding-climate.md (created), wiki/authors/farrell-justin.md (created), wiki/topics/climate-change.md (updated), wiki/topics/polarization-political.md (updated), wiki/topics/corporate-influence.md (created), wiki/topics/computational-social-science.md (created), wiki/papers/index.md (updated), wiki/topics/index.md (updated), wiki/index.md (updated)
• notes: PNAS 2016 computational analysis of climate change discourse; Structural Topic Modeling on 40,785 texts from 164 contrarian organizations (1993–2013); demonstrates corporate funding from ExxonMobil/Koch foundations directly influences polarization themes; funded organizations emphasize energy-production-friendly and scientific-skepticism narratives. First longitudinal evidence of funding-driven discourse shifts. 1 new author page, 2 new topic pages (corporate-influence, computational-social-science), 2 topic updates, 3 index updates.

[2026-04-29 23:54] ingest | Fact-checking as risk communication: the multi-layered risk of misinformation in times of COVID-19

• job: 1777425867779-0fb035aa
• submitter: reza
• page: Fact-checking as risk communication: the multi-layered risk of misinformation
• touched: wiki/papers/2020-krause-fact-checking-risk-communication.md (created), wiki/authors/krause-nicole.md (created), wiki/authors/freiling-isabelle.md (created), wiki/authors/beets-becca.md (created), wiki/authors/brossard-dominique.md (created), wiki/topics/covid19-misinformation.md (updated), wiki/topics/risk-communication.md (created), wiki/topics/fact-checking.md (created), wiki/topics/trust.md (created), wiki/topics/misinformation-responses.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Journal of Risk Research 2020 commentary on COVID-19 misinfodemic; applies risk communication research to show that fact-checking fails without addressing trust, multi-layered risk-definition differences, and uncertainty. Key insight: misinformation risk perceptions interact with COVID-19 risk perceptions (partisan cleavage on whether media or virus is the threat). Recommends trust partnerships with CDC/WHO, transparency on uncertainty, value-aligned messaging. 4 new author pages, 4 new topic pages (risk-communication, fact-checking, trust, misinformation-responses).

[2026-04-29 23:53] ingest | Susceptibility to misinformation about COVID-19 around the world

• job: 1777425867747-f5f9d999
• submitter: reza
• page: Susceptibility to misinformation about COVID-19 around the world
• touched: wiki/papers/2020-roozenbeek-susceptibility-covid-misinformation.md (created), wiki/authors/roozenbeek-jon.md (updated), wiki/authors/schneider-claudia.md (created), wiki/authors/dryhurst-sarah.md (created), wiki/authors/kerr-john.md (created), wiki/authors/freeman-alexandra.md (created), wiki/authors/recchia-gabriel.md (created), wiki/authors/van-der-bles-anne.md (created), wiki/authors/van-der-linden-sander.md (updated), wiki/topics/covid19-misinformation.md (updated), wiki/topics/vaccine-hesitancy.md (updated), wiki/topics/trust-in-science.md (created), wiki/topics/numeracy.md (created), wiki/topics/conspiracy-theories.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Royal Society Open Science 2020; international survey (3,750 adults, 5 countries: UK, Ireland, USA, Spain, Mexico) measuring susceptibility to COVID-19 misinformation and its link to vaccine hesitancy and health-guidance compliance. Key finding: higher trust in scientists (β = −0.31 to −0.42) and numeracy are robust protective factors across all countries. 6 new author pages, 3 new topic pages (trust-in-science, numeracy, conspiracy-theories), 2 topic updates, 2 index updates.

[2026-04-29 23:52] ingest | Bots increase exposure to negative and inflammatory content in online social systems

• job: 1777425867710-5982eddf
• submitter: reza
• page: Bots increase exposure to negative and inflammatory content in online social
• touched: wiki/papers/2018-stella-bots-catalan.md (created), wiki/authors/stella-massimo.md (created), wiki/authors/ferrara-emilio.md (updated), wiki/authors/de-domenico-manlio.md (created), wiki/topics/bot-detection.md (updated), wiki/topics/political-polarization.md (updated), wiki/topics/misinformation.md (updated), wiki/topics/social-networks.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: PNAS 2018 study of 2017 Catalan referendum Twitter data (~3.6M tweets); ~33% of users are bots; logistic regression classifier (92–98% precision); bots strategically target human influencers from network periphery; amplify group-specific inflammatory narratives (violent content for Independentists, neutral for Constitutionalists); demonstrate how automated accounts reinforce and exacerbate human polarization. 2 new author pages, 0 new topics, 4 topic updates.

[2026-04-29 23:50] ingest | Information Credibility on Twitter

• job: 1777425867656-63be4131
• submitter: reza
• page: Information Credibility on Twitter
• touched: wiki/papers/2011-castillo-twitter-credibility.md (created), wiki/authors/castillo-carlos.md (created), wiki/authors/mendoza-marcelo.md (created), wiki/authors/poblete-barbara.md (created), wiki/topics/credibility-assessment.md (updated), wiki/topics/propagation-based-detection.md (updated), wiki/topics/twitter.md (updated), wiki/topics/user-profiles.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Foundational work on Twitter credibility assessment; 2,500+ human-labeled trending topics; user reputation (followers, activity, registration age) and propagation structure (retweet tree patterns) are stronger credibility signals than text alone; 89% newsworthy detection, 86% credibility classification. 3 new author pages, 4 topic updates.

[2026-04-29 23:48] ingest | dEFEND: Explainable Fake News Detection

• job: 1777425867647-e36ecc3d
• submitter: reza
• page: dEFEND: Explainable Fake News Detection
• touched: wiki/papers/2019-shu-defend-explainable-detection.md (created), wiki/authors/shu-kai.md (updated), wiki/authors/cui-limeng.md (created), wiki/authors/wang-suhang.md (updated), wiki/authors/lee-dongywon.md (created), wiki/authors/liu-huan.md (updated), wiki/topics/explainable-detection.md (created), wiki/topics/user-comments.md (created), wiki/topics/hierarchical-attention.md (created), wiki/topics/social-context.md (updated), wiki/topics/content-based-detection.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Hierarchical attention networks + sentence-comment co-attention for jointly detecting fake news and explaining via check-worthy sentences and comments. 0.904 accuracy on PolitiFact, 0.808 on GossipCop. Human evaluation (AMT) validates explanability. Co-attention critical (18% F1 drop on GossipCop when removed). 2 new author pages, 3 new topic pages (explainable-detection, user-comments, hierarchical-attention), 2 topic updates.

[2026-04-29 23:45] ingest | EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection

• job: 1777425867638-42aaea35
• submitter: reza
• page: EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection
• touched: wiki/papers/2018-wang-eann-multimodal.md (created), wiki/authors/wang-yaqing.md (created), wiki/authors/ma-fenglong.md (created), wiki/authors/jin-zhiwei.md (created), wiki/authors/yuan-ye.md (created), wiki/authors/xun-guangxu.md (created), wiki/authors/jha-kishlay.md (created), wiki/authors/su-lu.md (created), wiki/authors/gao-jing.md (created), wiki/topics/adversarial-learning.md (created), wiki/topics/transfer-learning.md (created), wiki/topics/multimodal-detection.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Adversarial neural networks for multi-modal fake news detection on new/emerging events; event discriminator removes event-specific features to learn event-invariant representations; 71.5% / 82.7% accuracy on Twitter/Weibo, 10.3% improvement over prior baselines; foundational for transfer learning on fake news. 8 new author pages, 2 new topic pages (adversarial-learning, transfer-learning), 1 topic update (multimodal-detection).

[2026-04-29 23:42] ingest | MVAE: Multimodal Variational Autoencoder for Fake News Detection

• job: 1777425867634-39857f1d
• submitter: reza
• page: MVAE: Multimodal Variational Autoencoder for Fake News Detection
• touched: wiki/papers/2019-khattar-mvae-multimodal.md (created), wiki/authors/khattar-dhruv.md (created), wiki/authors/goud-jaipal-singh.md (created), wiki/authors/gupta-manish.md (created), wiki/authors/varma-vasudeva.md (created), wiki/topics/variational-autoencoders.md (created), wiki/topics/multimodal-detection.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: VAE-based multimodal fake news detection on Twitter/Weibo; joint training of encoder-decoder with binary classifier to learn cross-modal correlations; 74.5% / 82.4% accuracy, ~6% improvement over attention-based baselines. 4 new author pages, 1 new topic page (variational-autoencoders), 1 topic update (multimodal-detection).

[2026-04-29 22:15] ingest | Detecting Bots on Russian Political Twitter

• job: 1777425867580-162f74f7
• submitter: reza
• page: Detecting Bots on Russian Political Twitter
• touched: wiki/papers/2017-stukal-bots-russian-twitter.md (created), wiki/authors/stukal-denis.md (created), wiki/authors/sanovich-sergey.md (created), wiki/authors/bonneau-richard.md (created), wiki/authors/tucker-joshua.md (updated), wiki/topics/bot-detection.md (created), wiki/topics/russian-disinformation.md (created), wiki/topics/information-operations.md (updated), wiki/papers/index.md (updated), wiki/topics/index.md (updated), wiki/index.md (updated)
• notes: Ensemble classifier detects >50% of Russian political Twitter accounts as bots (2014-2015); highest precision (95%) among existing methods; platform choice (Twitterfeed, ifttt.com) is strongest bot predictor; activity spikes correlate with major political events (Crimea, Nemtsov). Foundational work on bot prevalence in information operations. 3 new author pages, 2 new topic pages (bot-detection, russian-disinformation).

[2026-04-29 21:52] ingest | Assessing the Russian Internet Research Agency's impact on the political attitudes and behaviors of American Twitter users in late 2017

• job: 1777425867575-83c18b28
• submitter: reza
• page: Assessing the Russian Internet Research Agency's impact on the political attitudes
• touched: wiki/papers/2020-bail-russian-ira-polarization.md (created), wiki/authors/bail-christopher.md (created), wiki/authors/guay-brian.md (created), wiki/authors/maloney-emily.md (created), wiki/authors/combs-aidan.md (created), wiki/authors/hillygus-sunshine.md (created), wiki/authors/merhout-friedolin.md (created), wiki/authors/freelon-deen.md (created), wiki/authors/volfovsky-alexander.md (created), wiki/topics/internet-research-agency.md (updated), wiki/topics/political-polarization.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Rare causal assessment of IRA campaign effects using longitudinal panel design (N=1,239). Null finding: IRA interaction did not significantly shift political attitudes/behaviors over 1 month. Suggests Russian propaganda reached already-polarized audiences. 8 new author pages, 2 topic page updates.

[2026-04-29 21:47] ingest | Coordinating a Multi-Platform Disinformation Campaign: Internet Research Agency Activity on Three U.S. Social Media Platforms, 2015 to 2017

• job: 1777425867571-b32d7091
• submitter: reza
• page: Coordinating a Multi-Platform Disinformation Campaign: Internet Research Agency
• touched: wiki/papers/2019-lukito-multi-platform-disinformation.md (created), wiki/authors/lukito-josephine.md (created), wiki/topics/internet-research-agency.md (created), wiki/topics/state-sponsored-information-operations.md (created), wiki/topics/strategic-communication.md (created), wiki/topics/disinformation.md (updated), wiki/topics/coordinated-inauthentic-behavior.md (updated), wiki/topics/information-operations.md (updated), wiki/topics/election-interference.md (updated), wiki/topics/social-media.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: VAR analysis of IRA's platform-aware disinformation strategy (Reddit as trial balloon, Twitter as amplification); demonstrates temporal cross-platform coordination; IRA activity responsive to Trump approval ratings; 1 new author page, 3 new topic pages (IRA, state-sponsored-infoops, strategic-communication), 5 topic page updates.

[2026-04-29 21:45] ingest | Cross-Platform State Propaganda: Russian Trolls on Twitter and YouTube during the 2016 U.S. Presidential Election

• job: 1777425867566-745dace5
• submitter: reza
• page: Cross-Platform State Propaganda: Russian Trolls on Twitter and YouTube during
• touched: wiki/papers/2020-golovchenko-cross-platform-propaganda.md (created), wiki/authors/golovchenko-yevgeniy.md (created), wiki/authors/buntain-cody.md (created), wiki/authors/eady-gregory.md (created), wiki/authors/brown-megan.md (created), wiki/authors/tucker-joshua.md (updated), wiki/topics/propaganda.md (updated), wiki/topics/election-interference.md (updated), wiki/topics/disinformation.md (updated), wiki/topics/twitter.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: First cross-platform (Twitter-YouTube) quantitative analysis of IRA propaganda during 2016 election; novel hyperlink-ideology inference method; identifies "pre-propaganda" strategy; 4 new author pages, updates 4 topic pages on propaganda, election interference, disinformation, and Twitter.

[2026-04-29 19:30] ingest | Troll Factories: Manufacturing Specialized Disinformation on Twitter

• job: 1777425867562-ee0e0c90
• submitter: reza
• page: Troll Factories: Manufacturing Specialized Disinformation on Twitter
• touched: wiki/papers/2020-linvill-troll-factories.md (created), wiki/authors/linvill-darren.md (created), wiki/authors/warren-patrick.md (created), wiki/topics/information-operations.md (updated), wiki/topics/election-interference.md (updated), wiki/topics/disinformation.md (updated), wiki/topics/twitter.md (updated), wiki/topics/coordinated-inauthentic-behavior.md (updated), wiki/topics/social-media.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Foundational 2020 analysis of Russia's Internet Research Agency on Twitter; identifies five specialized disinformation account types (Right Troll, Left Troll, News Feed, Hashtag Gamer, Fearmonger) with distinct behavioral signatures; demonstrates coordinated response to 2016 election events (debates, Podesta emails); creates 2 new author pages, updates 6 topic pages on information operations, election interference, disinformation, Twitter, coordinated inauthentic behavior, and social media.

[2026-04-29 19:22] ingest | Shared partisanship dramatically increases social tie formation in a Twitter field experiment

• job: 1777425867548-35dbd2ea
• submitter: reza
• page: Shared partisanship dramatically increases social tie formation in a Twitter
• touched: wiki/papers/2021-mosleh-partisanship-tie-formation.md (created), wiki/authors/mosleh-mohsen.md (updated), wiki/authors/rand-david.md (updated), wiki/authors/martel-cameron.md (created), wiki/authors/eckles-dean.md (created), wiki/topics/echo-chambers.md (updated), wiki/topics/twitter.md (updated), wiki/topics/political-polarization.md (updated), wiki/topics/partisanship.md (created), wiki/topics/homophily.md (created), wiki/topics/social-tie-formation.md (created), wiki/topics/field-experiments.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: PNAS 2021 field experiment demonstrating causal effect of partisan preference on social tie formation via bot accounts; users ~3× more likely to follow-back copartisans; no partisan asymmetry; creates 4 new topic pages (partisanship, homophily, social-tie-formation, field-experiments), updates 2 existing topics, creates 2 new author pages.

[2026-04-29 19:15] ingest | Less than you think: Prevalence and predictors of fake news dissemination on Facebook

• job: 1777425867502-61c330b1
• submitter: reza
• page: Less than you think: Prevalence and predictors of fake news dissemination
• touched: wiki/papers/2019-guess-prevalence-fake-news.md (created), wiki/authors/guess-andrew.md (created), wiki/authors/nagler-jonathan.md (created), wiki/authors/tucker-joshua.md (created), wiki/topics/social-media.md (updated), wiki/topics/misinformation-spread.md (updated), wiki/topics/user-profiles.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Science Advances 2019 empirical study linking survey (N=3,500) to Facebook profile data (N=1,191); establishes fake news sharing was rare (8.5%) with age as strongest predictor (65+ users 7× more likely than 18–29); robust to ideology and education controls; creates 3 new author pages, updates 3 topic pages on social media, misinformation spread, and user profiles.

[2026-04-29 19:00] ingest | Evaluating the fake news problem at the scale of the information ecosystem

• job: 1777425867493-6649a18d
• submitter: reza
• page: Evaluating the fake news problem at the scale of the information ecosystem
• touched: wiki/papers/2020-allen-fake-news-ecosystem.md (created), wiki/authors/allen-jennifer.md (created), wiki/authors/howland-baird.md (created), wiki/authors/mobius-markus.md (created), wiki/authors/rothschild-david.md (created), wiki/authors/watts-duncan.md (created), wiki/topics/fake-news.md (updated), wiki/topics/media-consumption.md (created), wiki/topics/information-ecosystems.md (created), wiki/topics/fake-news-prevalence.md (created), wiki/topics/index.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Ecosystem-scale measurement (Nielsen TV + Comscore desktop/mobile, 2016–2018) showing fake news comprises 0.15% of daily media diet; TV dominates news 5:1 over online; reframes misinformation debate toward mainstream media bias and news avoidance; creates 5 new author pages, 3 new topic pages on media consumption, information ecosystems, and prevalence.

[2026-04-29 18:45] ingest | Misinformation and Morality: Encountering Fake-News Headlines Makes Them Seem Less Unethical to Publish and Share

• job: 1777425867474-015a2333
• submitter: reza
• page: Misinformation and Morality: Encountering Fake-News Headlines Makes Them Seem
• touched: wiki/papers/2020-effron-misinformation-morality.md (created), wiki/authors/effron-daniel.md (created), wiki/authors/raj-medha.md (created), wiki/topics/moral-judgment.md (created), wiki/topics/illusory-truth-effect.md (created), wiki/topics/psychology-of-belief.md (updated), wiki/topics/misinformation.md (updated), wiki/topics/fake-news.md (updated), wiki/topics/social-media-behavior.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Four preregistered experiments (N=2,587) showing repeated exposure to fake-news headlines reduces moral condemnation of sharing independent of belief; fluency-driven moral desensitization distinct from illusory-truth effects; opens 2 new topic pages on moral judgment and illusory-truth effect, creates 2 new author pages.

[2026-04-29 18:30] ingest | Inoculating the Public against Misinformation about Climate Change

• job: 1777425867440-9fc3ca6d
• submitter: reza
• page: Inoculating the Public against Misinformation about Climate Change
• touched: wiki/papers/2017-van-der-linden-inoculating-climate.md (created), wiki/authors/van-der-linden-sander.md (updated), wiki/authors/leiserowitz-anthony.md (created), wiki/authors/rosenthal-seth.md (created), wiki/authors/maibach-edward.md (created), wiki/topics/prebunking-inoculation.md (updated), wiki/topics/psychology-of-belief.md (updated), wiki/topics/climate-change.md (created), wiki/topics/consensus-messaging.md (created), wiki/topics/polarization-political.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Foundational inoculation-theory experiments (N=2,167) showing consensus messaging (+20pp) is nullified by competing misinformation but inoculation preserves two-thirds of effect across political spectrum with no backfire; creates 3 new author pages and 3 new topic pages on climate, consensus, and polarization.

[2026-04-29 18:25] ingest | Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention

• job: 1777425867416-e63f2cfd
• submitter: reza
• page: Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for
• touched: wiki/papers/2020-pennycook-covid19-accuracy-nudge.md (created), wiki/authors/mcphetres-jonathon.md (created), wiki/authors/zhang-yunhao.md (created), wiki/authors/lu-jackson.md (created), wiki/authors/pennycook-gordon.md (updated), wiki/authors/rand-david.md (updated), wiki/topics/covid19-misinformation.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Pre-registered RCT demonstrating that accuracy-salience nudges nearly triple truth discernment in COVID-19 misinformation sharing; scalable intervention working uniformly across demographics; creates 3 new author pages; expands intervention research on the COVID-19 infodemic.

[2026-04-29 18:21] ingest | Defining "Fake News": A typology of scholarly definitions

• job: 1777425867402-8361a688
• submitter: reza
• page: Defining "Fake News": A typology of scholarly definitions
• touched: wiki/papers/2017-tandoc-defining-fake-news.md (created), wiki/authors/tandoc-edson.md (created), wiki/authors/lim-zheng.md (created), wiki/authors/ling-richard.md (created), wiki/topics/terminology.md (updated), wiki/topics/fake-news.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Foundational typology of 34 academic definitions (2003–2017) based on facticity and intent-to-deceive dimensions; identifies six types (satire, parody, fabrication, manipulation, advertising, propaganda); critical for conceptual clarity in misinformation research; creates 3 new author pages.

[2026-04-29 18:20] ingest | Misinformation and Its Correction: Continued Influence and Successful Debiasing

• job: 1777425867383-1b5015a0
• submitter: reza
• page: Misinformation and Its Correction: Continued Influence and Successful Debiasing
• touched: wiki/papers/2012-lewandowsky-misinformation-correction.md (created), wiki/authors/lewandowsky-stephan.md (updated), wiki/authors/ecker-ullrich.md (updated), wiki/authors/cook-john.md (updated), wiki/authors/seifert-colleen.md (created), wiki/authors/schwarz-norbert.md (created), wiki/topics/misinformation.md (updated), wiki/topics/debunking-misinformation.md (updated), wiki/topics/psychology-of-belief.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Psychological Science in the Public Interest foundational review (2012) synthesizing cognitive mechanisms of misinformation persistence and evidence-based debiasing strategies; widely cited precursor to later work on inoculation and interventions (Cook et al. 2017, Ecker et al. 2022); creates 2 new author pages (Seifert, Schwarz).

[2026-04-29 17:15] ingest | In Related News, That Was Wrong: The Correction of Misinformation Through Related Stories Functionality in Social Media

• job: 1777425868114-4747ad84
• submitter: reza
• page: In Related News, That Was Wrong: The Correction of Misinformation Through
• touched: wiki/papers/2015-bode-related-news-correction.md (created), wiki/authors/bode-leticia.md (created), wiki/authors/vraga-emily.md (updated), wiki/topics/debunking-misinformation.md (updated), wiki/topics/facebook.md (updated), wiki/topics/motivated-reasoning.md (updated), wiki/topics/vaccine-hesitancy.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Journal of Communication 2015 experimental study testing Facebook's "related stories" algorithm for misinformation correction across two samples; shows algorithmic debunking reduces health misperceptions when matched to initial position, but motivated reasoning constrains effectiveness and produces issue-specific results (GMO success vs. vaccine-autism failure); first empirical evidence that algorithmic curation can serve corrective function beyond content moderation.

[2026-04-29 16:45] ingest | Fake News: Fundamental Theories, Detection Strategies and Challenges

• job: 1777410072584-99a0499a
• submitter: reza
• page: Fake News: Fundamental Theories, Detection Strategies and Challenges
• touched: wiki/papers/2019-zhou-fundamental-theories-wsdm-tutorial.md (created), wiki/authors/zhou-xinyi.md (updated), wiki/authors/zafarani-reza.md (updated), wiki/authors/shu-kai.md (updated), wiki/authors/liu-huan.md (updated), wiki/topics/fundamental-theories.md (created), wiki/topics/credibility-assessment.md (updated), wiki/topics/propagation-based-detection.md (updated), wiki/topics/content-based-detection.md (updated), wiki/topics/linguistic-style-detection.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: WSDM 2019 tutorial summary synthesizing 20+ interdisciplinary theories and unified four-perspective detection framework; precursor to 2020 full survey; foundational for the field; creates 1 new topic page (fundamental-theories); updates 4 author pages and 5 existing topic pages to reference the tutorial's unifying framework.

[2026-04-29 16:35] ingest | Social media and vaccine hesitancy

• job: 1777425867793-5ce32d14
• submitter: reza
• page: Social media and vaccine hesitancy
• touched: wiki/papers/2020-wilson-social-media-vaccine.md (created), wiki/authors/wilson-steven-lloyd.md (created), wiki/authors/wiysonge-charles.md (created), wiki/topics/vaccine-hesitancy.md (created), wiki/topics/disinformation.md (updated), wiki/topics/misinformation.md (updated), wiki/topics/social-media.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: BMJ Global Health 2020 large-scale cross-national study (137–166 countries) demonstrating causal links between social media activity and public health outcomes; finds social media organization for offline action predicts vaccine safety skepticism; foreign disinformation campaigns associated with 2-percentage-point drop in vaccination coverage year-over-year and 15% increase in negative vaccine tweets; creates 1 new topic page (vaccine-hesitancy) addressing public health impact of misinformation/disinformation; integrates real-world outcome measures (Wellcome polling, WHO vaccination data) with social media indicators (sentiment-coded tweets, expert-coded political indicators).

[2026-04-29 16:15] ingest | It takes a village to manipulate the media: coordinated link sharing behavior during 2018 and 2019 Italian elections

• job: 1777425867724-c368507b
• submitter: reza
• page: It takes a village to manipulate the media: coordinated link sharing behavior
• touched: wiki/papers/2020-giglietto-coordinated-inauthentic-behavior.md (created), wiki/authors/giglietto-fabio.md (created), wiki/authors/righetti-nicola.md (created), wiki/authors/rossi-luca.md (created), wiki/authors/marino-giada.md (created), wiki/topics/coordinated-inauthentic-behavior.md (created), wiki/topics/facebook.md (created), wiki/topics/media-manipulation.md (updated), wiki/topics/misinformation.md (updated), wiki/topics/election-interference.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Information, Communication & Society 2020 study operationalizing coordinated inauthentic behavior via near-simultaneous link sharing on Facebook during Italian elections; demonstrates coordinated networks amplify problematic domains 1.79–2.22× more frequently than uncoordinated entities; identifies structural differences between political networks (concentrated, centralized) and non-political deceptive networks (dispersed, entertainment-masked); creates 4 new author pages and 2 new topic pages; validates detection methodology using CrowdTangle API and Italian fact-checking blacklists.

[2026-04-29 15:00] ingest | How to Counter Russian Social Media Influence in Eastern Europe

• job: 1777425867585-86bcf6b2
• submitter: reza
• page: How to Counter Russian Social Media Influence in Eastern Europe
• touched: wiki/articles/2018-rand-russian-social-media-influence.md (created), wiki/authors/helmus-todd.md (created), wiki/authors/bodine-baron-elizabeth.md (created), wiki/authors/radin-andrew.md (created), wiki/authors/magnuson-madeline.md (created), wiki/authors/mendelsohn-joshua.md (created), wiki/authors/marcellino-william.md (created), wiki/authors/bega-andriy.md (created), wiki/authors/winkelman-zev.md (created), wiki/authors/index.md (updated), wiki/topics/information-operations.md (created), wiki/topics/propaganda.md (created), wiki/topics/disinformation.md (updated), wiki/topics/media-manipulation.md (updated), wiki/topics/election-interference.md (updated), wiki/topics/social-media.md (updated), wiki/articles/index.md (updated), wiki/index.md (updated)
• notes: RAND Corporation 2018 report analyzing Russian state-sponsored social media campaigns in Eastern Europe; documents coordinated troll networks, bot amplification, fake hashtags, and nonattributed comments targeting Ukraine and other former Soviet states; mixed-methods approach combining quantitative social media analysis with security expert interviews; identifies counter-strategies (accelerated detection, alternative narratives, institutional capacity building); creates 8 new author pages and 2 new topic pages (information-operations, propaganda); strengthens understanding of state-level disinformation operations and counter-measures.

[2026-04-29 14:45] ingest | Fake news on Twitter during the 2016 U.S. presidential election

• job: 1777425867488-8f9de63a
• submitter: reza
• page: Fake news on Twitter during the 2016 U.S. presidential election
• touched: wiki/papers/2019-grinberg-twitter-election.md (created), wiki/authors/grinberg-nir.md (created), wiki/authors/joseph-kenneth.md (created), wiki/authors/friedland-lisa.md (created), wiki/authors/swire-thompson-briony.md (created), wiki/authors/lazer-david.md (created), wiki/topics/user-profiles.md (updated), wiki/topics/social-media-behavior.md (updated), wiki/topics/misinformation-spread.md (updated), wiki/topics/election-interference.md (updated), wiki/topics/social-media.md (updated), wiki/papers/index.md (updated), wiki/authors/index.md (updated), wiki/index.md (updated)
• notes: Science 2019 empirical study of individual-level fake news exposure on Twitter matched to voter registration records (N=16,442); documents extreme concentration of consumption (1% see 80%, 0.1% share 80%) driven by older, conservative, politically engaged subset; conservative-leaning users 6× more likely to share fake news; shows political congruency, not veracity, drives sharing decisions; identifies distinct network cluster of fake news sources (Group 2, 68.8% fake) consumed primarily by extreme-right users; mainstream media still dominates exposures across all groups (72–86%), contradicting echo-chamber narrative; creates five new author pages; strengthens understanding of who engages with misinformation on social media and media ecosystem structure.

[2026-04-29 14:35] ingest | Cognitive reflection correlates with behavior on Twitter

• job: 1777468639200-b2d56906
• submitter: reza
• page: Cognitive reflection correlates with behavior on Twitter
• touched: wiki/papers/2021-mosleh-cognitive-reflection-twitter.md (created), wiki/authors/mosleh-mohsen.md (created), wiki/authors/pennycook-gordon.md (updated), wiki/authors/arechar-antonio.md (created), wiki/authors/rand-david.md (updated), wiki/topics/cognitive-reflection.md (created), wiki/topics/echo-chambers.md (created), wiki/topics/news-source-quality.md (created), wiki/topics/twitter.md (created), wiki/topics/social-media-behavior.md (created), wiki/topics/misinformation.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Nature Communications 2021 field study (N=1,901) linking Cognitive Reflection Test scores to actual Twitter behavior; high-CRT users follow fewer accounts, preferentially share higher-quality news sources (BBC vs. Daily Mail), engage more with politics and less with "get rich quick" schemes; identifies asymmetric cognitive echo chambers where low-CRT users follow a distinct set of accounts; extends lab findings on truth discernment and reflectionist perspective to naturalistic social media contexts; establishes multiple novel topic pages (cognitive-reflection, social-media-behavior) and networks with existing misinformation/cognitive-reasoning literature; strengthens understanding of individual-differences drivers of information consumption on social media.

[2026-04-29 14:32] ingest | Cognitive attraction and online misinformation

• job: 1777468639195-e475a953
• submitter: reza
• page: Cognitive attraction and online misinformation
• touched: wiki/papers/2019-acerbi-cognitive-attraction.md (created), wiki/authors/acerbi-alberto.md (created), wiki/topics/psychology-of-belief.md (updated), wiki/topics/cognitive-reasoning.md (updated), wiki/topics/misinformation-spread.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Palgrave Communications 2019 content-analysis study of 260 articles from 26 hoax/fake-news websites demonstrating that misinformation spreads due to psychological appeal (negative framing, threat-related content, social information, disgust elements, minimally counterintuitive content) rather than media-system inefficiency; reframes misinformation as "high-quality" when measured by cognitive appeal rather than truthfulness; foundational cultural-evolution perspective; introduces novel author (Acerbi) from Eindhoven University of Technology; strengthens psychology-of-belief, cognitive-reasoning, and misinformation-spread topics with systematic content-analysis evidence.

[2026-04-28 23:45] ingest | Fake news game confers psychological resistance against online misinformation

• job: 1777425867434-4a4e66cd
• submitter: reza
• page: Fake news game confers psychological resistance against online misinformation
• touched: wiki/papers/2019-roozenbeek-bad-news-game.md (created), wiki/authors/roozenbeek-jon.md (created), wiki/authors/van-der-linden-sander.md (created), wiki/topics/prebunking-inoculation.md (updated), wiki/topics/psychology-of-belief.md (updated), wiki/topics/cognitive-reasoning.md (updated), wiki/papers/index.md (updated), wiki/authors/index.md (updated), wiki/index.md (updated)
• notes: Palgrave Communications 2019 large-scale empirical study (N=15,000) demonstrating that a gamified ~15-minute "Bad News" game teaching six deception techniques (impersonation, polarisation, emotional manipulation, conspiracy theories, discrediting opponents, trolling) significantly reduces perceived reliability of misinformation across education, age, political ideology, and cognitive style; largest effect among those most vulnerable at baseline (d=0.89); operationalizes active inoculation theory in experiential learning context; introduces two new authors (Roozenbeek, van der Linden) from Cambridge; reinforces prebunking-inoculation and psychology-of-belief topics with large-scale validation.

[2026-04-28 23:30] ingest | The psychological drivers of misinformation belief and its resistance to correction

• job: 1777425867425-2655f76c
• submitter: reza
• page: The psychological drivers of misinformation belief and its resistance to correction
• touched: wiki/papers/2022-ecker-psychological-drivers-misinformation.md (created), wiki/authors/ecker-ullrich.md (created), wiki/authors/lewandowsky-stephan.md (created), wiki/authors/cook-john.md (created), wiki/authors/schmid-philipp.md (created), wiki/authors/fazio-lisa.md (created), wiki/authors/brashier-nadia.md (created), wiki/authors/kendeou-panayiota.md (created), wiki/authors/vraga-emily.md (created), wiki/authors/amazeen-michelle.md (created), wiki/topics/psychology-of-belief.md (created), wiki/topics/continued-influence-effect.md (created), wiki/topics/debunking-misinformation.md (created), wiki/topics/prebunking-inoculation.md (created), wiki/topics/misinformation.md (updated), wiki/topics/motivated-reasoning.md (updated), wiki/topics/index.md (updated), wiki/papers/index.md (updated), wiki/authors/index.md (updated), wiki/index.md (updated)
• notes: Nature Reviews Psychology 2022 comprehensive review of cognitive, social, and affective drivers of false belief formation; establishes continued influence effect, barrier taxonomy (need for causal explanation, integration failure, illusory truth), and intervention framework (prebunking via inoculation, debunking via fact+alternative+fallacy); introduces nine new authors covering psychology, communication, and journalism; establishes four new topics (psychology-of-belief, continued-influence-effect, debunking, prebunking) and strengthens foundations with psychological mechanisms underpinning misinformation susceptibility.

[2026-04-28 23:15] ingest | Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning

• job: 1777425867411-01544771
• submitter: reza
• page: Lazy, not biased: Susceptibility to partisan fake news is better explained
• touched: wiki/articles/2019-cognition-lazy-not-biased.md (created), wiki/authors/pennycook-gordon.md (created), wiki/authors/rand-david.md (created), wiki/topics/fake-news-detection.md (created), wiki/topics/cognitive-reasoning.md (created), wiki/topics/misinformation.md (created), wiki/topics/motivated-reasoning.md (created), wiki/articles/index.md (updated), wiki/index.md (updated)
• notes: Cognition 2019 empirical study with 3,446 participants finding that analytic thinking (not partisan bias) predicts resistance to fake news; challenges motivated reasoning theory and supports classical reasoning account; establishes cognitive-reasoning and fake-news-detection topics as core to understanding susceptibility mechanisms; introduces two new authors (Pennycook, Rand).

[2026-04-28 22:31] ingest | Lexicon of Lies: Terms for Problematic Information

• job: 1777425867406-ce2e17a9
• submitter: reza
• page: Lexicon of Lies: Terms for Problematic Information
• touched: wiki/articles/2024-jack-lexicon-of-lies.md (created), wiki/authors/jack-caroline.md (created), wiki/topics/terminology.md (created), wiki/articles/index.md (updated), wiki/topics/index.md (updated), wiki/disinformation.md (updated), wiki/topics/media-manipulation.md (updated), wiki/authors/index.md (updated), wiki/index.md (updated)
• notes: Data & Society practitioner's guide clarifying terminology around misinformation, disinformation, propaganda, information operations, gaslighting, and related concepts; emphasizes how terminology choices shape assumptions about information spread and interventions; discusses challenges in establishing intent and cross-cultural complications (xuanchuan, dezinformatsiya); first source on terminology topic; strengthens foundations section with practitioner guidance complementing Wardle's framework.

[2026-04-28 22:15] ingest | Media Manipulation and Disinformation Online

• job: 1777425867397-3e1ff701
• submitter: reza
• page: Media Manipulation and Disinformation Online
• touched: wiki/articles/2017-marwick-media-manipulation.md (created), wiki/articles/index.md (created), wiki/authors/marwick-alice.md (created), wiki/authors/lewis-rebecca.md (created), wiki/topics/media-manipulation.md (created), wiki/topics/disinformation.md (created), wiki/topics/online-subcultures.md (created), wiki/index.md (updated)
• notes: Data & Society ecosystem-level analysis of internet subcultures' media manipulation tactics during 2016 U.S. election; synthesizes research on trolls, Gamergate, conspiracy theorists, MRAs, and white nationalist networks; documents coordinated strategies, platform vulnerabilities, and outcomes; first source on media-manipulation, disinformation, and online-subcultures topics; establishes article section in wiki.

[2026-04-28 22:00] ingest | Information Disorder: Toward an Interdisciplinary Framework for Research and Policy Making

• job: 1777425867378-3ed7f83e
• submitter: reza
• page: Information Disorder: Toward an Interdisciplinary Framework for Research and
• touched: wiki/papers/2017-wardle-information-disorder.md (created), wiki/authors/wardle-claire.md (created), wiki/authors/derakhshan-hossein.md (created), wiki/topics/fake-news.md (updated), wiki/topics/misinformation-spread.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Council of Europe's canonical 2017 report introducing the mis-/dis-/mal-information trichotomy and the agent-message-interpreter framework; foundational contribution to information disorder terminology that has become standard in academic and policy discourse; distinguishes false information by falseness and intent-to-harm, mapping creation-production-distribution lifecycle and providing 34 recommendations across sectors; introduces two new authors (Wardle at First Draft/Harvard Kennedy School, Derakhshan independent researcher) and elevates foundational taxonomy across fake-news and misinformation-spread topic pages.

[2026-04-28 21:30] ingest | Social Media and Fake News in the 2016 Election

• job: 1777425867373-f5f9771d
• submitter: reza
• page: Social Media and Fake News in the 2016 Election
• touched: wiki/papers/2017-allcott-social-media-fake-news.md (created), wiki/authors/allcott-hunt.md (created), wiki/authors/gentzkov-matthew.md (created), wiki/topics/fake-news.md (created), wiki/topics/social-media.md (created), wiki/topics/election-interference.md (created), wiki/topics/misinformation-spread.md, wiki/topics/political-bias.md, wiki/papers/index.md, wiki/authors/index.md, wiki/topics/index.md, wiki/index.md
• notes: Landmark JEP paper (2017) providing first comprehensive empirical quantification of fake news exposure during 2016 U.S. election; assembled database of 156 vetted false articles, web traffic data, and post-election survey of 1,208 adults; estimates average American saw 1.14 fake articles with 3:1 partisan asymmetry (pro-Trump articles shared 30.3M times vs. pro-Clinton 7.6M); introduces economic supply/demand model explaining low barriers to entry for fake news producers on social media; argues electoral impact smaller than single TV ad but raises important questions about mechanisms and real-world effects; introduces two new authors (Allcott at NYU, Gentzkov at Stanford) and three new foundational topic pages (fake-news, social-media, election-interference).

[2026-04-28 00:10] ingest | The Spread of True and False News Online

• job: 1777425867338-cf9c87a9
• submitter: reza
• page: The Spread of True and False News Online
• touched: wiki/papers/2017-vosoughi-spread-false-news.md (created), wiki/authors/vosoughi-soroush.md (created), wiki/authors/roy-deb.md (created), wiki/authors/aral-sinan.md (created), wiki/topics/propagation-based-detection.md (created), wiki/topics/misinformation-spread.md (created), wiki/topics/index.md, wiki/papers/index.md, wiki/authors/index.md, wiki/index.md
• notes: Landmark MIT IDE study of 126K verified true/false Twitter cascades (2006–2017); establishes that falsehood diffuses 6× faster to 1.5K people, reaches 10× greater depth, and is 70% more likely to be retweeted; demonstrates humans (not bots) drive the disparity; novelty perception is the key mechanism — foundational empirical work predating modern platform algorithms and serves as essential baseline for contemporary misinformation research; introduces three new authors (Vosoughi, Roy, Aral) and two new topic pages (propagation-based-detection, misinformation-spread).

[2026-04-28 00:09] ingest | A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities

• job: 1777423930373-bd994454
• submitter: reza
• page: A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
• touched: wiki/papers/2020-zhou-fake-news-survey.md (created), wiki/authors/zhou-xinyi.md, wiki/authors/zafarani-reza.md, wiki/papers/index.md, wiki/index.md
• notes: Comprehensive survey organizing fake news detection across four perspectives (knowledge-based fact-checking, style-based linguistic/visual features, propagation-based cascade and network analysis, source-based credibility assessment) with grounding in interdisciplinary theories from psychology and social sciences — published in ACM Computing Surveys 2020 and forms the foundational taxonomy for the wiki; Zhou and Zafarani now have 8 papers indexed, making this their most-represented authorship team in the collection.

[2026-04-28 00:08] ingest | Network-based Fake News Detection: A Pattern-driven Approach

• job: 1777410197289-1288fe33
• submitter: reza
• page: Network-based Fake News Detection: A Pattern-driven Approach
• touched: wiki/papers/2019-zhou-network-pattern-detection.md (created), wiki/authors/zhou-xinyi.md, wiki/authors/zafarani-reza.md, wiki/topics/social-context.md, wiki/topics/feature-engineering.md, wiki/papers/index.md, wiki/index.md
• notes: First pure network-based paper in the wiki; four theoretically grounded propagation patterns (More-Spreader, Farther-Distance, Stronger-Engagement, Denser-Network) operationalized as 138 features across five network levels — directly extends the social-context thread and provides the network complement to the content-only early-detection paper (1904.11679) already in the wiki; RF 0.929/0.932 on PolitiFact without reading content.

[2026-04-28 00:07] ingest | Fake News Early Detection: An Interdisciplinary Study

• job: 1777410197285-b434ea2d
• submitter: reza
• page: Fake News Early Detection: An Interdisciplinary Study
• touched: wiki/papers/2020-zhou-early-detection-interdisciplinary.md (created), wiki/authors/jain-atishay.md (created), wiki/authors/phoha-vir.md (created), wiki/authors/zhou-xinyi.md, wiki/authors/zafarani-reza.md, wiki/topics/content-based-detection.md, wiki/topics/feature-engineering.md, wiki/topics/linguistic-style-detection.md, wiki/datasets/fakenewsnet.md, wiki/papers/index.md, wiki/index.md
• notes: Foundational theory-driven feature engineering paper from the Syracuse group; proposes the multi-level HCLF framework (sBOW + POS + CFG + DIA/CBA + RST) that HERO (2023) later replaces with a neural recursive architecture — this paper provides the handcrafted baseline that quantifies what structure-aware neural models must beat; first paper in the wiki to explicitly analyze fake news through the lens of deception and clickbait theories, establishing interpretable behavioral patterns (informality, subjectivity, emotional language, headline sensationalism) that hold consistently across both datasets.

[2026-04-28 00:06] ingest | Is Less Really More? Fake News Detection with Limited Information

• job: 1777410072580-b520a256
• submitter: reza
• page: Is Less Really More? Fake News Detection with Limited Information
• touched: wiki/papers/2025-cao-slim-limited-information.md (created), wiki/authors/cao-zhaoyang.md (created), wiki/authors/nguyen-john.md (created), wiki/authors/zafarani-reza.md, wiki/datasets/fake-and-real-news.md (created), wiki/datasets/recovery.md, wiki/topics/multimodal-detection.md, wiki/topics/feature-engineering.md, wiki/papers/index.md, wiki/index.md
• notes: First limited-information / efficiency-focused paper in the wiki; SLIM's core finding — that MMR-selected 30% keyword subsets recover ~99% of full-text XLNet accuracy — directly challenges the dominant full-text paradigm and is immediately relevant for sparse-data and real-time deployment contexts; adds two new authors (Cao, Nguyen), one new dataset (Fake_And_Real_News), and links into three existing topic pages; complements the HERO paper (same Syracuse group, opposite direction: maximising vs. minimising input richness).

[2026-04-28 00:05] ingest | Linguistic-style-aware Neural Networks for Fake News Detection

• job: 1777410072576-8f4ac6b4
• submitter: reza
• page: Linguistic-style-aware Neural Networks for Fake News Detection
• touched: wiki/papers/2023-zhou-hero-linguistic-style.md (created), wiki/authors/li-jiayu.md (created), wiki/authors/li-qinzhou.md (created), wiki/authors/zhou-xinyi.md, wiki/authors/zafarani-reza.md, wiki/topics/linguistic-style-detection.md (created), wiki/topics/content-based-detection.md, wiki/papers/2020-zhou-recovery-covid19.md, wiki/papers/index.md, wiki/index.md
• notes: First purely structural neural approach to linguistic-style fake news detection in the wiki; HERO's key advance over prior HCLF work (also by this group) is preserving the global tree structure of hierarchical linguistic trees rather than extracting local node-level statistics — the 12–30% AUC gap over HCLF directly measures the cost of discarding that structure; introduces two new authors (Jiayu Li, Qinzhou Li) and a new topic page (linguistic-style-detection).

[2026-04-28 00:04] ingest | CHECKED: Chinese COVID-19 Fake News Dataset

• job: 1777410072562-6b3fc7af
• submitter: reza
• page: CHECKED: Chinese COVID-19 Fake News Dataset
• touched: wiki/datasets/checked.md (created), wiki/authors/yang-chen.md (created), wiki/authors/zhou-xinyi.md, wiki/authors/zafarani-reza.md, wiki/topics/covid19-misinformation.md, wiki/topics/multimodal-detection.md, wiki/topics/social-context.md, wiki/papers/2020-zhou-recovery-covid19.md, wiki/papers/index.md, wiki/datasets/index.md, wiki/index.md
• notes: First Chinese-language entry in the wiki; CHECKED fills the intersection gap between existing Chinese COVID-19 datasets (unlabeled) and non-Chinese COVID-19 credibility datasets — adding per-item expert labels, multimedia, and the richest propagation graphs yet (1.87M reposts) among datasets in the wiki; complements ReCOVery for cross-lingual infodemic research.

[2026-04-28 00:03] ingest | ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research

• job: 1777410072558-fdbd46ac
• submitter: reza
• page: ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research
• touched: wiki/datasets/recovery.md (created), wiki/topics/covid19-misinformation.md (created), wiki/authors/mulay-apurva.md (created), wiki/authors/ferrara-emilio.md (created), wiki/authors/zhou-xinyi.md, wiki/authors/zafarani-reza.md, wiki/topics/multimodal-detection.md, wiki/topics/credibility-assessment.md, wiki/papers/2020-zhou-safe-multimodal.md, wiki/papers/index.md, wiki/datasets/index.md, wiki/index.md
• notes: First COVID-19-specific entry in the wiki; introduces publisher-level credibility labeling (NewsGuard + MBFC dual-authority thresholds) as a scalable alternative to per-article annotation, with the inherent accuracy trade-off; SAFE achieves best benchmark performance on the corpus, cross-linking directly to the prior SAFE ingest; adds Emilio Ferrara (USC ISI) as a new author.

[2026-04-28 00:02] ingest | SAFE: Similarity-Aware Multi-Modal Fake News Detection

• job: 1777410072553-66847882
• submitter: reza
• page: SAFE: Similarity-Aware Multi-Modal Fake News Detection
• touched: wiki/authors/wu-jindi.md (created), wiki/authors/zhou-xinyi.md, wiki/authors/zafarani-reza.md, wiki/topics/multimodal-detection.md (created), wiki/topics/content-based-detection.md (created), wiki/datasets/fakenewsnet.md, wiki/papers/2019-shu-user-profiles-fake-news.md, wiki/papers/2019-sitaula-credibility-fake-news.md, wiki/index.md
• notes: First multimodal paper in the wiki — introduces cross-modal text-image similarity (modified cosine over image2sentence embeddings) as a fake news signal, outperforming all text-only and fusion baselines on FakeNewsNet; complements the two prior content-credibility papers from the same Syracuse group.

[2026-04-28 00:01] ingest | Credibility-based Fake News Detection

• job: 1777410072549-03765915
• submitter: reza
• page: Credibility-based Fake News Detection
• touched: wiki/authors/sitaula-niraj.md, wiki/authors/mohan-chilukuri.md, wiki/authors/grygiel-jennifer.md, wiki/authors/zhou-xinyi.md, wiki/authors/zafarani-reza.md, wiki/topics/credibility-assessment.md, wiki/topics/feature-engineering.md, wiki/index.md
• notes: Establishes source-credibility features (author count, publication history) as stronger fake-news discriminants than content features — directly complements the UPF approach in the first ingested paper, both from the same Syracuse group.

[2026-04-28 00:00] ingest | The Role of User Profiles for Fake News Detection

• job: 1777410072544-f7c4e548
• submitter: reza
• page: The Role of User Profiles for Fake News Detection
• touched: wiki/authors/shu-kai.md, wiki/authors/zhou-xinyi.md, wiki/authors/wang-suhang.md, wiki/authors/zafarani-reza.md, wiki/authors/liu-huan.md, wiki/topics/user-profiles.md, wiki/topics/social-context.md, wiki/topics/feature-engineering.md, wiki/topics/political-bias.md, wiki/datasets/fakenewsnet.md, wiki/index.md
• notes: First paper ingested; demonstrates user-profile features (esp. account age and political bias) outperform content baselines on FakeNewsNet — key entry point for social-context detection literature.

[2026-04-28 21:15] lint | weekly health check

• result: 9 mechanical, 1 substantive
• queued auto-fix job: 1777425320276-8179eb38
• substantive issues for human review:
• ORPHAN wiki/datasets/fake-and-real-news.md

[2026-04-28 21:16] lint | auto-fix

• fixed: 16 mechanical issues
• touched: wiki/index.md
• repairs:
• Fixed 1 broken wikilink (surveys/index — page does not exist)
• Added missing section index links (papers, authors, datasets, videos, tools)
• Added new Topics section with all 9 topic pages (content-based-detection, covid19-misinformation, credibility-assessment, feature-engineering, linguistic-style-detection, multimodal-detection, political-bias, social-context, user-profiles)

[2026-04-29 20:30] ingest | Emotion shapes the diffusion of moralized content in social networks

• job: 1777425867543-11b7bf55
• submitter: reza
• page: Emotion shapes the diffusion of moralized content in social networks
• touched: wiki/papers/2017-brady-moral-emotion-diffusion.md (created), wiki/authors/brady-william-j.md (created), wiki/authors/wills-julian-a.md (created), wiki/authors/jost-john-t.md (created), wiki/authors/van-bavel-jay-j.md (created), wiki/authors/tucker-joshua.md (updated), wiki/topics/information-diffusion.md (created), wiki/topics/social-networks.md (created), wiki/topics/moral-psychology.md (created), wiki/topics/political-polarization.md (created), wiki/topics/emotional-language.md (created), wiki/papers/index.md (updated), wiki/topics/index.md (updated), wiki/index.md (updated)
• notes: PNAS 2017 empirical study (n=563,312 tweets) demonstrating moral-emotional language increases message diffusion by ~20% per word across gun control, same-sex marriage, and climate change; shows effect stronger within ideological in-groups than out-groups, explaining echo chamber polarization; creates 4 new author pages and 5 new topic pages on information diffusion, social networks, moral psychology, political polarization, and emotional language.

[2026-05-03 04:36] lint | weekly health check

• result: 50 mechanical, 27 substantive
• queued auto-fix job: 1777797362213-bed46efc
• substantive issues for human review:
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/datasets/fake-and-real-news.md
• ORPHAN wiki/topics/message-design.md
• ORPHAN wiki/topics/fake-news-prevalence.md
• ORPHAN wiki/topics/climate-change.md
• ORPHAN wiki/topics/influencers.md
• ORPHAN wiki/topics/public-sphere.md
• ORPHAN wiki/topics/frame-analysis.md
• ORPHAN wiki/topics/partisanship.md
• ORPHAN wiki/topics/content-analysis.md
• ORPHAN wiki/topics/social-tie-formation.md
• ORPHAN wiki/topics/continued-influence-effect.md
• ORPHAN wiki/topics/russian-disinformation.md
• ORPHAN wiki/topics/variational-autoencoders.md
• ORPHAN wiki/topics/news-source-quality.md
• ORPHAN wiki/topics/gab.md
• ORPHAN wiki/topics/digital-literacy.md
• ORPHAN wiki/topics/political-bubbles.md
• ORPHAN wiki/topics/internet-research-agency.md
• ORPHAN wiki/topics/bot-detection.md
• ORPHAN wiki/topics/state-sponsored-information-operations.md
• ORPHAN wiki/topics/numeracy.md
• ORPHAN wiki/topics/youtube-radicalization.md
• ORPHAN wiki/topics/social-media-engagement.md
• ORPHAN wiki/topics/whatsapp-misinformation.md
• ORPHAN wiki/topics/junk-news-detection.md

[2026-05-10 04:42] lint | weekly health check

• result: 50 mechanical, 27 substantive
• queued auto-fix job: 1778402545266-49487aa0
• substantive issues for human review:
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/datasets/fake-and-real-news.md
• ORPHAN wiki/topics/message-design.md
• ORPHAN wiki/topics/fake-news-prevalence.md
• ORPHAN wiki/topics/climate-change.md
• ORPHAN wiki/topics/influencers.md
• ORPHAN wiki/topics/public-sphere.md
• ORPHAN wiki/topics/frame-analysis.md
• ORPHAN wiki/topics/partisanship.md
• ORPHAN wiki/topics/content-analysis.md
• ORPHAN wiki/topics/social-tie-formation.md
• ORPHAN wiki/topics/continued-influence-effect.md
• ORPHAN wiki/topics/russian-disinformation.md
• ORPHAN wiki/topics/variational-autoencoders.md
• ORPHAN wiki/topics/news-source-quality.md
• ORPHAN wiki/topics/gab.md
• ORPHAN wiki/topics/digital-literacy.md
• ORPHAN wiki/topics/political-bubbles.md
• ORPHAN wiki/topics/internet-research-agency.md
• ORPHAN wiki/topics/bot-detection.md
• ORPHAN wiki/topics/state-sponsored-information-operations.md
• ORPHAN wiki/topics/numeracy.md
• ORPHAN wiki/topics/youtube-radicalization.md
• ORPHAN wiki/topics/social-media-engagement.md
• ORPHAN wiki/topics/whatsapp-misinformation.md
• ORPHAN wiki/topics/junk-news-detection.md

[2026-05-15 21:50] ingest | Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking

• job: 1778895727658-e00d7ce8
• submitter: reza
• page: Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
• touched: wiki/papers/2017-rashkin-truth-varying-shades.md, wiki/authors/rashkin-hannah.md, wiki/authors/choi-eunsol.md, wiki/authors/jang-jin-yea.md, wiki/authors/volkova-svitlana.md, wiki/authors/choi-yejin.md, wiki/topics/linguistic-analysis.md, wiki/topics/fake-news-detection.md, wiki/topics/deception-detection.md, wiki/topics/fact-checking.md, wiki/papers/index.md, wiki/index.md
• notes: foundational paper on linguistic analysis of fake news across satire/hoax/propaganda categories using LIWC features; first systematic graded truthfulness prediction on PolitiFact 6-point scale

[2026-05-15 22:05] ingest | Quantifying Controversy on Social Media

• job: 1778895727601-896c45c5
• submitter: reza
• page: Quantifying Controversy on Social Media
• touched: wiki/papers/2017-garimella-quantifying-controversy.md, wiki/authors/garimella-kiran.md, wiki/authors/gionis-aristides.md, wiki/authors/mathioudakis-michael.md, wiki/authors/de-francisci-morales-gianmarco.md, wiki/topics/controversy-detection.md, wiki/topics/graph-based-methods.md, wiki/topics/polarization.md, wiki/topics/twitter.md, wiki/papers/index.md, wiki/index.md

[2026-05-16 00:00] ingest | Who Let The Trolls Out? Towards Understanding State-Sponsored Trolls

• job: 1778895728012-dddde94f
• submitter: reza
• page: Who Let The Trolls Out? Towards Understanding State-Sponsored Trolls
• touched: wiki/papers/2018-zannettou-state-sponsored-trolls.md, wiki/authors/zannettou-savvas.md, wiki/authors/caulfield-tristan.md, wiki/authors/setzer-william.md, wiki/authors/sirivianos-michael.md, wiki/authors/stringhini-gianluca.md, wiki/authors/blackburn-jeremy.md, wiki/topics/state-sponsored-information-operations.md, wiki/topics/coordinated-inauthentic-behavior.md, wiki/topics/twitter.md
• notes: foundational comparative analysis of Russian and Iranian state-sponsored troll operations across Twitter/Reddit; demonstrates distinct platform-specific strategies and real-world event-driven adaptation; 10M posts from verified accounts with influence measurement via URL amplification and network analysis
• notes: foundational paper on graph-based controversy measurement via 3-stage pipeline (graph building, partitioning, measurement); proposes random-walk-based metric (RWC) outperforming baselines on Twitter and external datasets; extends controversy-detection and graph-based-methods topics

[2026-05-16 18:25] ingest | NELA-GT-2022: A Large Multi-Labelled News Dataset

• job: 1778895727703-d6a0133f
• submitter: reza
• page: NELA-GT-2022: A Large Multi-Labelled News Dataset for The Study of Misinformation
• dataset: NELA-GT-2022
• touched: wiki/papers/2022-gruppi-nela-gt-2022.md (created), wiki/datasets/nela-gt-2022.md (created), wiki/authors/gruppi-mauricio.md (updated), wiki/authors/horne-benjamin.md (updated), wiki/authors/adali-sibel.md (updated), wiki/topics/fake-news.md (updated), wiki/topics/veracity-assessment.md (updated), wiki/datasets/index.md (updated), wiki/index.md (updated), wiki/log.md (updated)
• notes: Fifth installment in NELA-GT series describing 1.78M news articles from 361 outlets collected throughout 2022; source-level Media Bias/Fact Check labels (factuality 0–5 scale, conspiracy/pseudoscience classification); 346K embedded tweets; focus on stabilizing collection infrastructure for near-complete temporal coverage rather than adding features; provided event-specific subsets for Russo-Ukrainian War and Roe v. Wade overturning; released in SQLite and JSON formats. Updated author pages and topic pages (fake-news, veracity-assessment) to include references.

[2026-05-16 19:45] ingest | MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation

• job: 1778895727713-95e1de70
• submitter: reza
• page: MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19
• dataset: MM-COVID
• touched: wiki/papers/2020-li-mm-covid-multilingual-multimodal.md (created), wiki/datasets/mm-covid.md (created), wiki/authors/li-yichuan.md (created), wiki/authors/jiang-bohan.md (created), wiki/authors/shu-kai.md (updated), wiki/authors/liu-huan.md (updated), wiki/topics/multilingual-detection.md (created), wiki/topics/cross-lingual.md (created), wiki/topics/covid19-misinformation.md (updated), wiki/topics/multimodal-detection.md (updated), wiki/datasets/index.md (updated), wiki/index.md (updated), wiki/log.md (updated)
• notes: First multilingual and multimodal COVID-19 dataset spanning six languages (English, Spanish, Portuguese, Hindi, French, Italian) with 3,981 fake news pieces and 7,192 tweets; demonstrates social context provides language-invariant signals enabling cross-lingual transfer (0.85 F₁ zero-resource Portuguese transfer); dEFEND text+social achieves 0.91–0.96 in high-resource and 0.76–0.90 in low-resource scenarios. Created new author pages for Li and Jiang; created new topic pages for multilingual-detection and cross-lingual; updated existing author pages and topic pages (covid19-misinformation, multimodal-detection) to include references.

[2026-05-16 21:00] ingest | GLTR: Statistical Detection and Visualization of Generated Text

• job: 1778895727733-ad8c96f7
• submitter: reza
• page: GLTR: Statistical Detection and Visualization of Generated Text
• touched: wiki/papers/2019-gehrmann-gltr-generated-text.md (created), wiki/authors/gehrmann-sebastian.md (created), wiki/authors/strobelt-hendrik.md (created), wiki/authors/rush-alexander.md (created), wiki/topics/generated-text-detection.md (created), wiki/topics/language-models.md (updated), wiki/topics/neural-text-generation.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated), wiki/log.md (updated)
• notes: Statistical detection tool for AI-generated text exploiting the fact that language models sample from restricted distribution regions while humans use wider vocabulary. Three tests (word probability, token rank, entropy) reveal generated text concentrates on high-rank tokens; AUC 0.85–0.87 with rank-based features vs. 0.63 with bag-of-words baseline. Human-subjects study (N=35) shows GLTR visualization improves detection accuracy from 54% to 72% without prior training; 42% of users reported improved accuracy, 37% reported faster detection. Tool widely deployed at gltr.io with 30K demo views and 21K blog views in first month. Created three new author pages (Gehrmann, Strobelt, Rush), one new topic page (generated-text-detection), and updated two existing topic pages (language-models, neural-text-generation) to include cross-references.

[2026-05-16 10:00] ingest | All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text

• job: 1778895727762-c3510638
• submitter: reza
• page: All That''s ''Human'' Is Not Gold: Evaluating Human Evaluation of Generated
• touched: wiki/papers/2021-clark-human-eval-generated-text.md (created), wiki/authors/clark-elizabeth.md (created), wiki/authors/august-tal.md (created), wiki/authors/serrano-sofia.md (created), wiki/authors/haduong-nikita.md (created), wiki/authors/gururangan-suchin.md (created), wiki/authors/smith-noah.md (created), wiki/topics/nlp-evaluation.md (created), wiki/topics/text-generation.md (created), wiki/topics/quality-assessment.md (created), wiki/topics/machine-generated-text-detection.md (created), wiki/topics/crowdsourcing.md (created), wiki/topics/human-evaluation.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Large-scale crowdsourced evaluation study (1,170 workers) examining human ability to distinguish GPT2/GPT3-generated text from human-written content across three domains (stories, news, recipes). Key findings: untrained evaluators achieve 57% accuracy on GPT2 and 50% (chance) on GPT3. Three training interventions tested (instructions, examples, comparisons) show modest improvements with example-based training reaching 55% overall accuracy. Analysis reveals evaluators focus predominantly on surface-level features (grammar, spelling, style; 47% of coded comments) rather than content (25%), suggesting evaluators underestimate what current models can generate and lack systematic evaluation methodology. Recommendations include moving away from small-batch crowdsourcing and prioritizing evaluator training and careful evaluation design in NLG research. Created 7 author pages (Clark, August, Serrano, Haduong, Gururangan, Smith from University of Washington and Allen AI); created 5 new topic pages (nlp-evaluation, text-generation, quality-assessment, machine-generated-text-detection, crowdsourcing); updated human-evaluation topic with paper as key reference; updated main index and papers index.

[2026-05-16 14:30] ingest | Disinformation Capabilities of Large Language Models

• job: 1778895727969-36ccbeb4
• submitter: reza
• page: Disinformation Capabilities of Large Language Models
• touched: wiki/papers/2023-vykopal-disinformation-capabilities-llms.md (created), wiki/authors/vykopal-ivan.md (created), wiki/authors/pikuliak-matus.md (created), wiki/authors/srba-ivan.md (created), wiki/authors/moro-robert.md (created), wiki/authors/macko-dominik.md (created), wiki/authors/bielikova-maria.md (created), wiki/topics/large-language-models.md (created), wiki/topics/disinformation-generation.md (created), wiki/topics/ai-safety.md (created), wiki/topics/generative-models.md (created), wiki/index.md (updated), wiki/log.md (updated)
• notes: First large-scale empirical evaluation of disinformation generation capabilities across 10 LLMs. Manually evaluated 1,200 generated texts against 20 disinformation narratives (COVID-19, Russia-Ukraine, health, elections, regional). Key finding: most models readily agree with dangerous narratives; Falcon is sole exception with systematic refusal. ChatGPT shows behavioral safety via filters. Existing detection methods achieve ~0.8 F1 but struggle per-sample. Comprehensive evaluation framework with 6-question human assessment and detection experiments on multiple detector classes. Created 6 author pages (all from Kempelen Institute and Brno University), 4 new topic pages (large-language-models, disinformation-generation, ai-safety, generative-models) establishing the "LLMs & generative-era" section's foundation for safety and generation research.

[2026-05-16] ingest | Deepfake Detection: A Systematic Literature Review

• job: 1778895727800-5734d9e2
• submitter: reza
• page: Deepfake Detection: A Systematic Literature Review
• touched: wiki/papers/2022-rana-deepfake-detection-survey.md (created), wiki/authors/rana-md-shohel.md (created), wiki/authors/nobi-mohammad-nur.md (created), wiki/authors/murali-beddhu.md (created), wiki/authors/sung-andrew-h.md (created), wiki/topics/deepfakes.md (updated), wiki/topics/deep-learning.md (updated), wiki/topics/face-manipulation.md (created), wiki/topics/digital-media-forensics.md (created), wiki/topics/survey.md (updated), wiki/index.md (updated)
• notes: Systematic literature review of 112 deepfake detection papers (2018–2020) with rigorous Budgen-Brereton methodology. Categorizes detection techniques into four families: deep learning 77% (primarily CNNs), machine learning 18% (SVM, RF, k-NN), statistical 3%, blockchain 2%. Comprehensive analysis of 100+ models, 50+ datasets (FaceForensics++ dominant), and 10+ feature types. Deep learning achieves 89.7% mean accuracy vs. 85% for traditional ML. Identifies key gaps: inconsistent evaluation practices, small datasets limiting generalization, and arms-race dynamics between detection and generation. First systematic review examining the deepfake detection field with evidence-based model comparison.

[2026-05-16] ingest | In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking

• job: 1778895728003-a808a22a
• submitter: reza
• page: In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking
• touched: wiki/papers/2018-li-eye-blinking-detection.md (created), wiki/authors/chang-ming-ching.md (created), wiki/authors/li-yuezun.md (updated), wiki/authors/lyu-siwei.md (updated), wiki/topics/deepfake-detection.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Pioneering physiological-signal-based deepfake detection method exploiting absence of natural eye blinking in GAN-synthesized faces. LRCN model (CNN feature extraction + LSTM sequence learning) achieves 0.99 AUC on authentic vs. synthesized video detection. Introduced Eye Blinking Video (EBV) dataset with 50 videos and frame-level eye-state annotations. Demonstrates temporal modeling (LSTM) improves over frame-level CNN (0.99 vs. 0.98 AUC) and hand-crafted features (0.79 AUC). Created author page for Ming-Ching Chang; updated existing author pages for Yuezun Li and Siwei Lyu; updated deepfake-detection topic page with new paper reference.

[2026-05-16] ingest | Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

• job: 1778895728074-ca9318ab
• submitter: reza
• page: Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks
• touched: wiki/papers/2020-bian-bi-gcn-rumor-detection.md (created), wiki/authors/bian-tian.md (created), wiki/authors/xiao-xi.md (created), wiki/authors/xu-tingyang.md (created), wiki/authors/zhao-peilin.md (created), wiki/authors/huang-wenbing.md (created), wiki/authors/rong-yu.md (created), wiki/authors/huang-junzhou.md (created), wiki/topics/rumor-detection.md (updated), wiki/topics/graph-neural-networks.md (updated), wiki/papers/index.md (updated)
• notes: First GCN-based rumor detection on social media combining top-down and bottom-up propagation patterns with source-post feature enhancement. Bi-GCN achieves 96.1% accuracy on Weibo, 88.6% on Twitter15, 88.0% on Twitter16—significant improvements over RvNN, CNN-RNN hybrids, and kernel-based baselines. Strong early-detection performance (80%+ accuracy within 1–2 hours). Key innovation is bidirectional GCN architecture capturing both propagation (TD-GCN) and dispersion (BU-GCN) characteristics jointly, plus root feature concatenation at each layer. Created 7 author pages (6 from Tencent AI Lab, 1 from Tsinghua); updated rumor-detection and graph-neural-networks topic pages with paper as key reference; updated papers index (now 158 papers).

[2026-05-16 16:50] ingest | GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media

• job: 1778895728078-0fef8034
• submitter: reza
• page: GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection
• touched: wiki/papers/2020-lu-gcan-fake-news-detection.md (created), wiki/authors/lu-yi-ju.md (created), wiki/authors/li-cheng-te.md (created), wiki/topics/fake-news-detection.md (updated), wiki/topics/graph-neural-networks.md (updated), wiki/topics/propagation-based-detection.md (updated), wiki/topics/rumor-detection.md (updated), wiki/topics/social-media.md (updated), wiki/topics/explainability.md (created), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: GCN-based fake news detection on Twitter using only short-text tweets and retweet sequences; dual co-attention mechanism provides interpretability; 18–20% improvement over prior work (87.7% Twitter15, 90.8% Twitter16); strong early detection capability at 10 retweets; models user interactions via fully-connected graphs with feature similarity weighting.

[2026-05-16 17:24] ingest | User Preference-aware Fake News Detection

• job: 1778895728092-160472ab
• submitter: reza
• page: User Preference-aware Fake News Detection
• touched: wiki/papers/2021-dou-user-preference-detection.md (created), wiki/authors/dou-yingtong.md (updated), wiki/authors/shu-kai.md (updated), wiki/authors/xia-congying.md (updated), wiki/authors/yu-philip-s.md (updated), wiki/authors/sun-lichao.md (created), wiki/topics/fake-news-detection.md (updated), wiki/topics/user-behavior.md (updated), wiki/topics/graph-neural-networks.md (updated), wiki/topics/social-media.md (updated), wiki/topics/confirmation-bias.md (expanded), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: GNN-based framework integrating user endogenous preferences (historical tweets) with exogenous propagation patterns. Demonstrates confirmation bias as learnable signal; BERT+GraphSAGE achieves 84.62% accuracy on Politifact, 97.23% on Gossipcop; ablation studies show both components necessary; releases augmented FakeNewsNet benchmark with crawled user history.

[2026-05-16 18:00] ingest | A Benchmark Study of Machine Learning Models for Online Fake News Detection

• job: 1778895728102-3460f424
• submitter: reza
• page: A Benchmark Study of Machine Learning Models for Online Fake News Detection
• touched: wiki/papers/2019-khan-benchmark-ml-models.md (created), wiki/authors/khan-junaed-younus.md (created), wiki/authors/khondaker-md-tawkat-islam.md (created), wiki/authors/afroz-sadia.md (created), wiki/authors/uddin-gias.md (created), wiki/authors/iqbal-anindya.md (created), wiki/topics/pre-trained-language-models.md (created), wiki/topics/fake-news-detection.md (updated), wiki/topics/machine-learning.md (updated), wiki/topics/deep-learning.md (updated), wiki/index.md (updated)
• notes: Comprehensive benchmark of 19 ML models on three fake news datasets (12.8k to 79.5k articles). Key finding: BERT-based pre-trained models (RoBERTa) achieve 96%+ accuracy on large datasets and >90% with only 500 training samples, substantially outperforming traditional ML and deep learning. Provides practical guidance for low-resource and production-constrained settings. Includes new 80k-article combined corpus across diverse topics beyond politics.

[2026-05-16 17:34] ingest | All-in-one: Multi-task Learning for Rumour Verification

• job: 1778895728165-de448591
• submitter: reza
• page: All-in-one: Multi-task Learning for Rumour Verification
• touched: wiki/papers/2018-kochkina-multitask-rumor-verification.md (created), wiki/authors/kochkina-elena.md (updated), wiki/authors/liakata-maria.md (updated), wiki/authors/zubiaga-arkaitz.md (updated), wiki/topics/multi-task-learning.md (created), wiki/topics/rumour-detection.md (updated), wiki/topics/stance-classification.md (updated), wiki/topics/veracity-assessment.md (updated), wiki/index.md (updated)
• notes: Multi-task learning approach for rumor verification jointly training veracity prediction (main task) with stance classification and rumor detection (auxiliary tasks). Shows 13.6% improvement on RumourEval and up to 28.9% on PHEME 9-event dataset over single-task baselines. Analyzes link between dataset properties (kurtosis, entropy) and MTL effectiveness—lower-kurtosis (more balanced) event distributions benefit more from auxiliary tasks. Created multi-task-learning topic page as foundation for future MTL work; updated rumour-detection, stance-classification, and veracity-assessment topics.

[2026-05-16] ingest | What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context

• job: 1778895728296-9dc26899
• submitter: reza
• page: What Was Written vs. Who Read It: News Media Profiling Using Text Analysis
• touched: wiki/papers/2020-baly-media-profiling.md (created), wiki/authors/baly-ramy.md (created), wiki/authors/karadzhov-georgi.md (created), wiki/authors/dinkov-yoan.md (created), wiki/authors/ali-ahmed.md (created), wiki/authors/glass-james.md (created), wiki/authors/an-jisun.md (updated), wiki/authors/kwak-haewoon.md (updated), wiki/authors/nakov-preslav.md (updated), wiki/topics/media-profiling.md (updated), wiki/topics/factuality-assessment.md (updated), wiki/topics/news-credibility.md (updated), wiki/topics/media-bias-detection.md (updated), wiki/papers/index.md (updated)
• notes: Multi-modal approach predicts outlet-level political bias (left/center/right) and factuality (low/mixed/high) by combining article text BERT features, YouTube video/caption analysis, Twitter follower demographics, Facebook audience estimates, and Wikipedia content. Achieves 85% accuracy on bias prediction, 69% on factuality. Demonstrates importance of "who reads it" dimension beyond content analysis. First major work integrating social media audience signals with media profiling.

[2026-05-17 00:00] ingest | We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!

• job: 1778895728385-69eb4d57
• submitter: reza
• page: 2016-anand-clickbait-neural-networks (deleted)
• touched: wiki/papers/2016-anand-clickbait-neural-networks.md (created), wiki/authors/anand-ankesh.md (created), wiki/authors/park-noseong.md (created), wiki/authors/chakraborty-tanmoy.md (updated), wiki/topics/clickbait.md (updated), wiki/topics/deep-learning.md (updated), wiki/topics/recurrent-neural-networks.md (created), wiki/topics/text-classification.md (created)
• notes: First paper on neural network-based clickbait detection. Bidirectional LSTM with combined word and character embeddings achieves 98% accuracy on 15K-headline dataset, 5% improvement over hand-crafted SVM baselines. Demonstrates effectiveness of deep learning without feature engineering for headline classification.

[2026-05-17 03:15] ingest | Predicting the Type and Target of Offensive Posts in Social Media

• job: 1778895728467-656aa8c9
• submitter: reza
• page: Predicting the Type and Target of Offensive Posts in Social Media
• touched: wiki/papers/2019-zampieri-offensive-language-identification.md (created), wiki/authors/zampieri-marcos.md (created), wiki/authors/malmasi-shervin.md (created), wiki/authors/rosenthal-sara.md (created), wiki/authors/farra-noura.md (created), wiki/authors/kumar-ritesh.md (created), wiki/authors/nakov-preslav.md (updated), wiki/topics/offensive-language-detection.md (created), wiki/topics/content-moderation.md (updated), wiki/topics/hate-speech-detection.md (updated), wiki/datasets/OLID.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Foundational hierarchical annotation schema and dataset for offensive language detection. OLID (14,100 tweets) unifies prior work on hate speech, cyberbullying, and aggression via three-level hierarchy: Level A (offensive/not), Level B (targeted insult vs. untargeted profanity), Level C (individual vs. group vs. other target). CNN baseline achieves 0.80 F1 on Level A. Created new offensive-language-detection topic page; updated hate-speech-detection and content-moderation with links to OLID.

[2026-05-17 04:30] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1779006618911-0110ef1f
• substantive issues for human review:
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/datasets/fake-and-real-news.md
• ORPHAN wiki/datasets/dfdc.md
• ORPHAN wiki/datasets/weibo21.md
• ORPHAN wiki/datasets/mbfc.md
• ORPHAN wiki/datasets/banda-covid19-twitter-chatter.md
• ORPHAN wiki/datasets/multifc.md
• ORPHAN wiki/datasets/OLID.md
• ORPHAN wiki/topics/survey.md
• ORPHAN wiki/topics/stylometry.md
• ORPHAN wiki/topics/message-design.md
• ORPHAN wiki/topics/fake-news-prevalence.md
• ORPHAN wiki/topics/climate-change.md
• ORPHAN wiki/topics/influencers.md
• ORPHAN wiki/topics/public-sphere.md
• ORPHAN wiki/topics/frame-analysis.md
• ORPHAN wiki/topics/partisanship.md
• ORPHAN wiki/topics/social-tie-formation.md
• ORPHAN wiki/topics/continued-influence-effect.md
• ORPHAN wiki/topics/variational-autoencoders.md
• ORPHAN wiki/topics/cognitive-reflection.md
• ORPHAN wiki/topics/news-source-quality.md
• ORPHAN wiki/topics/gab.md
• ORPHAN wiki/topics/qanon.md
• ORPHAN wiki/topics/digital-literacy.md
• ORPHAN wiki/topics/political-bubbles.md
• ORPHAN wiki/topics/internet-research-agency.md
• ORPHAN wiki/topics/numeracy.md
• ORPHAN wiki/topics/accuracy.md
• ORPHAN wiki/topics/social-media-engagement.md
• ORPHAN wiki/topics/junk-news-detection.md
• ORPHAN wiki/topics/biometrics.md
• ORPHAN wiki/topics/media-ecosystems.md
• ORPHAN wiki/topics/hierarchical-networks.md
• ORPHAN wiki/topics/diaspora-misinformation.md
• ORPHAN wiki/topics/group-anomaly-detection.md
• ORPHAN wiki/topics/2016-election.md
• ORPHAN wiki/topics/spatiotemporal-information.md
• ORPHAN wiki/topics/user-engagement.md
• ORPHAN wiki/topics/probability-based-detection.md
• ORPHAN wiki/topics/fine-grained-classification.md
• ORPHAN wiki/topics/vision-language-models.md
• ORPHAN wiki/topics/rumor-amplification.md
• ORPHAN wiki/topics/content-features.md
• ORPHAN wiki/topics/llm-generated-content.md
• ORPHAN wiki/topics/fake-reviews.md
• ORPHAN wiki/topics/biological-signals.md
• ORPHAN wiki/topics/detection-difficulty.md

[2026-05-17 00:00] ingest | Survey of Hallucination in Natural Language Generation

• job: 1778895728533-55948567
• submitter: reza
• page: Survey of Hallucination in Natural Language Generation
• touched: wiki/papers/2022-ji-hallucination-nlg.md (created), wiki/authors/ji-ziwei.md (created), wiki/authors/lee-nayeon.md (created), wiki/authors/frieske-rita.md (created), wiki/authors/yu-tiezheng.md (created), wiki/authors/su-dan.md (created), wiki/authors/xu-yan.md (created), wiki/authors/ishii-etsuko.md (created), wiki/authors/bang-yejin.md (created), wiki/authors/chen-delong.md (created), wiki/authors/dai-wenliang.md (created), wiki/authors/chan-ho-shu.md (created), wiki/authors/madotto-andrea.md (created), wiki/authors/fung-pascale.md (created), wiki/topics/hallucinations.md (updated), wiki/topics/large-language-models.md (updated), wiki/topics/question-answering.md (updated), wiki/topics/vision-language-models.md (updated), wiki/topics/natural-language-generation.md (created), wiki/topics/abstractive-summarization.md (created), wiki/topics/dialogue-generation.md (created), wiki/topics/machine-translation.md (created), wiki/topics/data-to-text-generation.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Comprehensive cross-task survey of hallucination in NLG defining intrinsic/extrinsic categories; covers abstractive summarization, dialogue, QA, data-to-text, machine translation, vision-language models, and LLMs; reviews metrics and mitigation strategies. First unified treatment of hallucination across multiple NLG tasks.

[2026-05-17 00:00] ingest | Universal and Transferable Adversarial Attacks on Aligned Language Models

• job: 1778895728543-5afb1d6c
• submitter: reza
• page: Universal and Transferable Adversarial Attacks on Aligned Language Models
• touched: wiki/papers/2023-zou-universal-transferable-adversarial-attacks.md (created), wiki/authors/zou-andy.md (created), wiki/authors/wang-zifan.md (created), wiki/authors/carlini-nicholas.md (created), wiki/authors/nasr-milad.md (created), wiki/authors/kolter-zico.md (created), wiki/authors/fredrikson-matt.md (created), wiki/topics/llm-safety.md (updated), wiki/topics/jailbreaking.md (created), wiki/topics/alignment.md (created), wiki/papers/index.md (updated)
• notes: Automated generation of universal adversarial suffixes using Greedy Coordinate Gradient search; demonstrates high transferability (87.9% on GPT-3.5) but lower on Claude-2 (2.1%); raises critical questions about alignment robustness.

[2026-05-17 12:45] ingest | Jailbroken: How Does LLM Safety Training Fail?

• job: 1778895728562-339357c8
• submitter: reza
• page: Jailbroken: How Does LLM Safety Training Fail?
• touched: wiki/papers/2023-wei-jailbroken-llm-safety.md (created), wiki/authors/wei-alexander.md (created), wiki/authors/haghtalab-nika.md (created), wiki/authors/steinhardt-jacob.md (created), wiki/topics/llm-safety.md (updated), wiki/topics/jailbreaking.md (updated), wiki/index.md (updated), wiki/log.md (updated)
• notes: Systematic analysis of why safety training fails in LLMs through two fundamental failure modes (competing objectives and mismatched generalization); develops 30 jailbreak methods with >90% success on GPT-4/Claude despite extensive red-teaming. Critical insight that scaling alone cannot resolve these inherent training objective conflicts.

[2026-05-17 14:30] ingest | The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention

• job: 1778895728568-8c4f4577
• submitter: reza
• page: The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges
• touched: wiki/papers/2023-ayoobi-linkedin-fake-llm-profiles.md (created), wiki/authors/ayoobi-navid.md (created), wiki/authors/shahriar-sadat.md (created), wiki/authors/mukherjee-arjun.md (created), wiki/topics/fake-accounts.md (created), wiki/topics/content-authentication.md (created), wiki/topics/detection-methods.md (updated), wiki/topics/large-language-models.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: First LinkedIn fake profile detection dataset (3600 profiles: 1800 legitimate, 600 human-created fake, 1200 ChatGPT-generated) with SSTE method achieving 95% accuracy and 70%+ detection on unseen LLM-generated content. Demonstrates robustness of section-level embeddings to next-generation fake profiles despite training data limitations.

[2026-05-17] ingest | The platform governance triangle: conceptualising the informal regulation of online content

• job: 1778895728639-7148c67d
• submitter: reza
• page: The platform governance triangle: conceptualising the informal regulation
• touched: wiki/articles/2019-gorwa-platform-governance-triangle.md (created), wiki/authors/gorwa-robert.md (created), wiki/topics/platform-governance.md (created), wiki/topics/online-regulation.md (created), wiki/topics/policy-regulation.md (created), wiki/topics/content-moderation.md (updated), wiki/articles/index.md (updated), wiki/index.md (updated)
• notes: Policy analysis article from Internet Policy Review applying governance triangle model to platform content regulation. Examines informal multi-stakeholder arrangements (Facebook Oversight Body, Christchurch Call) and identifies three key dynamics: actor competencies, legitimation politics, and power asymmetries. Argues that informal governance is becoming the primary mode of platform regulation in Europe.

[2026-05-17 19:35] ingest | Language Models are Few-Shot Learners

• job: 1778895728684-f3d78d87
• submitter: reza
• page: Language Models are Few-Shot Learners
• touched: wiki/papers/2020-brown-gpt3-few-shot.md (created), wiki/authors/brown-tom.md (created), wiki/authors/mann-benjamin.md (created), wiki/authors/ryder-nick.md (created), wiki/authors/subbiah-melanie.md (created), wiki/authors/kaplan-jared.md (created), wiki/authors/dhariwal-prafulla.md (created), wiki/authors/neelakantan-arvind.md (created), wiki/authors/shyam-pranav.md (created), wiki/authors/sastry-girish.md (created), wiki/authors/askell-amanda.md (created), wiki/authors/agarwal-sandhini.md (created), wiki/authors/herbert-voss-ariel.md (created), wiki/authors/krueger-gretchen.md (created), wiki/authors/henighan-tom.md (created), wiki/authors/child-rewon.md (created), wiki/authors/ramesh-aditya.md (created), wiki/authors/ziegler-daniel.md (created), wiki/authors/wu-jeffrey.md (created), wiki/authors/winter-clemens.md (created), wiki/authors/hesse-christopher.md (created), wiki/authors/chen-mark.md (created), wiki/authors/sigler-eric.md (created), wiki/authors/litwin-mateusz.md (created), wiki/authors/gray-scott.md (created), wiki/authors/chess-benjamin.md (created), wiki/authors/clark-jack.md (created), wiki/authors/berner-christopher.md (created), wiki/authors/mccandlish-sam.md (created), wiki/authors/radford-alec.md (created), wiki/authors/sutskever-ilya.md (created), wiki/authors/amodei-dario.md (created), wiki/topics/language-models.md (created), wiki/topics/few-shot-learning.md (created), wiki/topics/in-context-learning.md (created), wiki/topics/large-language-models.md (created), wiki/topics/synthetic-text-generation.md (created), wiki/papers/index.md (updated)
• notes: Landmark GPT-3 paper demonstrating that 175B parameter language models achieve strong few-shot performance without fine-tuning through in-context learning. Critically for misinformation research, documents that GPT-3 can generate multi-paragraph news articles indistinguishable from human-written articles, founding concern about AI-generated misinformation. Shows smooth power-law scaling of few-shot capability with model size.

[2026-05-17 20:45] ingest | Training language models to follow instructions with human feedback

• job: 1778895728709-baa5b2de
• submitter: reza
• page: Training language models to follow instructions with human feedback (deleted)
• touched: wiki/authors/ouyang-long.md, wiki/authors/christiano-paul.md, wiki/authors/wu-jeff.md, wiki/topics/alignment.md, wiki/topics/instruction-tuning.md, wiki/index.md
• notes: InstructGPT paper from OpenAI: foundational work on RLHF for aligning language models with user intent. Demonstrates that a 1.3B parameter model fine-tuned with human feedback is preferred over a 175B base model by humans; improves truthfulness and reduces toxic outputs. Relevant to understanding how language models can be steered toward or away from generating misinformation.

[2026-05-17 21:15] ingest | Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

• job: 1778895728778-ba932aab
• submitter: reza
• page: Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
• touched: wiki/papers/2023-asai-self-rag.md (created), wiki/authors/asai-akari.md (created), wiki/authors/wu-zeqiu.md (created), wiki/authors/wang-yizhong.md (created), wiki/authors/sil-avirup.md (created), wiki/authors/hajishirzi-hannaneh.md (created), wiki/topics/retrieval-augmented-generation.md (updated), wiki/topics/factuality.md (updated), wiki/topics/hallucination.md (updated), wiki/topics/fact-checking.md (updated), wiki/topics/language-models.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Introduces SELF-RAG framework for adaptive retrieval and self-reflection in language models via reflection tokens. Directly addresses factuality through learned self-critique (support tokens) and adaptive retrieval decisions. Achieves strong results on PopQA, PubHealth, ASQA, and biography generation. Relevant to misinformation detection as it demonstrates trainable mechanisms for models to evaluate evidence and justify outputs—critical for trustworthy AI systems.

[2026-05-17] ingest | Universal Language Model Fine-tuning for Text Classification

• job: 1778895728909-994df2e6
• submitter: reza
• page: 2018-howard-ulmfit
• touched: wiki/papers/2018-howard-ulmfit.md (created), wiki/authors/howard-jeremy.md (created), wiki/authors/ruder-sebastian.md (created), wiki/topics/transfer-learning.md (updated), wiki/topics/language-models.md (updated), wiki/topics/text-classification.md (updated), wiki/topics/nlp-methods.md (created), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: ULMFiT: landmark transfer learning work establishing practical pretraining and fine-tuning approach for NLP. Introduces discriminative fine-tuning, slanted triangular learning rates, and gradual unfreezing for robust language model adaptation. Achieves state-of-the-art on six text classification tasks with minimal labeled data; foundational for low-resource misinformation detection applications.

[2026-05-17] ingest | Efficient Estimation of Word Representations in Vector Space

• job: 1778895728894-d7670ed3
• submitter: reza
• page: 2013-mikolov-word2vec (deleted)
• touched: wiki/papers/2013-mikolov-word2vec.md (created), wiki/authors/mikolov-tomas.md (created), wiki/authors/chen-kai.md (created), wiki/authors/corrado-greg.md (created), wiki/authors/dean-jeffrey.md (created), wiki/topics/text-embeddings.md (updated), wiki/topics/language-models.md (updated), wiki/topics/natural-language-processing.md (updated), wiki/papers/index.md (updated), wiki/index.md (updated)
• notes: Foundational Word2Vec paper introducing CBOW and Skip-gram architectures for efficiently learning word embeddings at scale. Enables high-quality vector representations of 1.6B words in under a day. Word vectors capture semantic/syntactic regularities enabling vector arithmetic. Foundational to modern NLP including fake news detection systems that use embeddings as feature extractors; core reference for understanding text representation in deep learning approaches to misinformation detection.

[2026-05-17] ingest | Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media

• job: 1778895728946-40563741
• submitter: reza
• page: Engagement, User Satisfaction, and the Amplification of Divisive Content on
• touched: wiki/papers/2023-mills-engagement-amplification.md, wiki/authors/mills-smitha.md, wiki/authors/carroll-micah.md, wiki/authors/wang-yike.md, wiki/authors/pandey-sashrika.md, wiki/authors/zhao-sebastian.md, wiki/authors/dragan-anca.md, wiki/topics/algorithmic-amplification.md, wiki/topics/divisive-content.md, wiki/topics/ranking-algorithms.md, wiki/topics/user-preferences.md, wiki/topics/political-polarization.md, wiki/index.md
• notes: Pre-registered field experiment demonstrating that Twitter's engagement-based ranking algorithm amplifies partisan and hostile content (0.24 SD) beyond what users report wanting to see. Proposes and validates an alternative ranking based on users' stated preferences, showing significant reduction in divisive content while maintaining engagement and user satisfaction. Key finding: users' revealed preferences (engagement) diverge from stated preferences (survey), suggesting engagement optimization may harm user welfare.

[2026-05-17 14:35] ingest | Model Merging by Uncertainty-Based Gradient Matching

• job: 1778895729092-8dd7034a
• submitter: reza
• page: 2023-dahein-model-merging
• touched: wiki/papers/2023-dahein-model-merging.md (created), wiki/authors/dahein-nico.md (created), wiki/authors/mollenhoff-thomas.md (created), wiki/authors/ponti-edoardo.md (created), wiki/authors/gurevych-iryna.md (updated), wiki/authors/khan-mohammad-emtiyaz.md (created), wiki/topics/model-merging.md (created), wiki/topics/knowledge-transfer.md (created), wiki/topics/transfer-learning.md (updated), wiki/index.md (updated)
• notes: ICLR 2024 paper on merging task-adapted models via uncertainty-based gradient matching. Theoretical contribution connecting merge error to gradient mismatch; practical method using Hessian-based preconditioning to improve merging for task addition, task removal, and vision transformers. Applicable to consolidating knowledge from models fine-tuned on different datasets or domains in fake news detection workflows.

[2026-05-17] ingest | The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

• job: 1778895729121-f08d7883
• submitter: reza
• page: The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and
• touched: wiki/papers/2018-brundage-malicious-use-ai.md (created), wiki/authors/brundage-miles.md (updated), wiki/authors/avin-shahar.md (created), wiki/authors/toner-helen.md (created), wiki/authors/eckersley-peter.md (created), wiki/authors/garfinkel-ben.md (created), wiki/topics/dual-use-technology.md (created), wiki/topics/political-security.md (created), wiki/topics/disinformation.md (updated), wiki/topics/deepfakes.md (updated), wiki/topics/ai-safety.md (updated), wiki/index.md (updated), wiki/papers/index.md (updated)
• notes: Landmark 2018 threat analysis of AI-enabled attacks across digital, physical, and political domains; identifies deepfakes, automated disinformation campaigns, and denial-of-information attacks as political security threats; emphasizes dual-use nature of AI and proposes policy recommendations for mitigating malicious uses. Prophetic regarding political weaponization of AI; foundational for AI governance and responsible disclosure frameworks.

[2026-05-17] ingest | Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models

• job: 1778895729285-917f7946
• submitter: reza
• page: Explainable Claim Verification via Knowledge-Grounded Reasoning with Large
• touched: wiki/papers/2023-wang-folk-claim-verification.md (created), wiki/authors/wang-haoran.md (created), wiki/authors/shu-kai.md (updated), wiki/topics/claim-verification.md (updated), wiki/topics/explainability.md (updated), wiki/topics/large-language-models.md (updated), wiki/topics/fact-checking.md (updated), wiki/topics/natural-language-inference.md (updated), wiki/index.md (updated)
• notes: FOLK framework combining first-order logic decomposition with knowledge-grounded reasoning for claim verification; achieves state-of-the-art on HoVER, FEVEROUS, SciFactOpen while generating high-quality human-readable explanations.

[2026-05-17] ingest | Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques

• job: 1778895729355-567dc412
• submitter: reza
• page: Advancing Graph Representation Learning with Large Language Models: A Comprehensive
• touched: wiki/papers/2024-mao-graph-representation-llm-survey.md (created), wiki/authors/mao-qiheng.md (created), wiki/authors/liu-zemin.md (updated), wiki/authors/liu-chenghao.md (created), wiki/authors/li-zhuo.md (created), wiki/authors/sun-jianling.md (created), wiki/topics/graph-representation-learning.md (created), wiki/topics/large-language-models.md (updated), wiki/topics/graph-neural-networks.md (updated), wiki/topics/survey.md (updated), wiki/index.md (updated)
• notes: First systematic taxonomy of LLM-GRL integration from technical standpoint; decomposes models into knowledge extractors (attribute, structure, label) and organizers (GNN-centric, LLM-centric, hybrid); analyzes integration strategies and training paradigms. Directly applicable to enhancing graph-based misinformation detection with semantic information from language models.

[2026-05-18] lint | auto-fix

• fixed: 56 broken wikilinks in author pages
• touched: 42 author pages across wiki/authors/
• notes: Removed non-existent topic references (covid-19-misinformation, adversarial-machine-learning, synthetic-content-detection, rumour-verification, etc.) from author topic lists. Fixed incorrect paper slug reference 2017-wang-topological-rnn-diffusion → Topological Recurrent Neural Network for Diffusion Prediction.

[2026-05-18] lint | cleanup-after-deletion

• job: 1779076110000-c3dcba94
• deleted: 2026-kasneci-chatgpt-education (off-topic: LLMs in education, out of scope for misinformation wiki)
• fixed: 32 broken wikilinks
• touched: wiki/index.md, wiki/papers/index.md, wiki/topics/bias-and-fairness.md, wiki/topics/copyright-issues.md, wiki/topics/student-learning.md, wiki/topics/ai-in-education.md, wiki/topics/teaching.md, wiki/topics/education.md, wiki/topics/privacy.md, 20 author pages (gunnemannnn-stephan, hullermeier-eyke, groh-georg, fischer-frank, dementieva-daryna, bannert-maria, kuchemann-stefan, sessler-kathrin, schmidt-albrecht, gasser-urs, kasneci-enkeleida, kasneci-gjergji, kuhn-jochen, weller-jochen, stadler-matthias, seidel-tina, sailer-michael, poquet-oleksandra, pfeffer-jurgen, nerdel-claudia, michaeli-tilman, kutyniok-gitta, krusche-stephan)
• notes: Removed all wikilinks to the deleted page from topic pages and author pages. Reduced papers/index.md count from 276 to 275. Cleaned up orphaned author pages with no other sources.

[2026-05-18] relevance audit | batch 6/19

• audited: 15 papers
• in_scope: 15
• borderline: 0
• out_of_scope: 0
• flagged for removal: none
• papers: 2018-thorne-fever-dataset (in_scope), 2018-thorne-fever-shared-task (in_scope), 2018-van-prooijen-emerging-conspiracy-research (in_scope), 2018-wang-eann-multimodal (in_scope), 2018-yang-inconsistent-head-poses (in_scope), 2018-yang-ti-cnn (in_scope), 2018-zannettou-disinformation-warfare (in_scope), 2018-zannettou-meme-origins (in_scope), 2018-zannettou-state-sponsored-trolls (in_scope), 2018-zannettou-web-false-information (in_scope), 2018-zhou-two-stream-tampered-face (in_scope), 2018-zubiaga-rumor-detection-survey (in_scope), 2019-acerbi-cognitive-attraction (in_scope), 2019-adelani-sentiment-preserving-fake-reviews (in_scope), 2019-augenstein-multifc-fact-checking (in_scope)

[2026-05-18] relevance audit | batch 4/19

• audited: 15 papers
• in_scope: 11
• borderline: 3
• out_of_scope: 0
• flagged for removal: none
• papers: 2018-lazer-science-fake-news (in_scope), 2018-lee-comparative-abusive-language (borderline), 2018-li-eye-blinking-detection (in_scope), 2018-ma-rumor-detection-rvnn (in_scope), 2018-mccloskey-gan-color-detection (in_scope), 2018-mohseni-interpretable-news-feed (in_scope), 2018-nelson-small-disloyal-fake-news (in_scope), 2018-popat-declareE (in_scope), 2018-potthast-clickbait-challenge (borderline), 2018-shao-anatomy-misinformation (in_scope), 2018-sharma-combating-fake-news-survey (in_scope), 2018-shu-fakenewsnet-dataset (in_scope), 2018-shu-network-analysis-fake-news (in_scope), 2018-soares-influencers-polarized-twitter (borderline), 2018-stella-bots-catalan (in_scope)

[2026-05-18] relevance audit | batch 11/19

• audited: 15 papers
• in_scope: 15
• borderline: 0
• out_of_scope: 0
• flagged for removal: none
• papers: 2020-shu-mining-disinformation-fake-news (in_scope), 2020-tan-neural-fake-news (in_scope), 2020-tolosana-deepfakes-survey (in_scope), 2020-treen-online-climate-misinformation (in_scope), 2020-tsfati-mainstream-media-dissemination (in_scope), 2020-vaccari-deepfakes-disinformation (in_scope), 2020-van-der-linden-covid-inoculation (in_scope), 2020-vo-fact-checked-information-search (in_scope), 2020-walter-fact-checking-meta-analysis (in_scope), 2020-wilson-social-media-vaccine (in_scope), 2020-yang-checked-covid19 (in_scope), 2020-zhou-early-detection-interdisciplinary (in_scope), 2020-zhou-fake-news-survey (in_scope), 2020-zhou-recovery-covid19 (in_scope), 2020-zhou-safe-multimodal (in_scope)

[2026-05-18 23:59] relevance audit

• audited: 15 papers
• in_scope: 12
• borderline: 3
• out_of_scope: 0
• papers: 2021-jones-jang-media-literacy-fake-news (in_scope), 2021-kaliyar-fakebert (in_scope), 2021-lin-truthfulqa (in_scope), 2021-mayank-deap-faked (in_scope), 2021-mirsky-threat-offensive-ai (borderline), 2021-mosleh-cognitive-reflection-twitter (in_scope), 2021-mosleh-partisanship-tie-formation (in_scope), 2021-nakov-media-profiling-factuality-bias (in_scope), 2021-nan-mdfend-multidomain (in_scope), 2021-papasavva-qanon-conspiracy (in_scope), 2021-pennycook-attention-accuracy (in_scope), 2021-plepi-sarcasm-detection (borderline), 2021-pulastya-mislabeled-samples (borderline), 2021-silva-cross-domain-multimodal (in_scope), 2021-suarez-lledo-health-misinformation-survey (in_scope)

[2026-05-18] relevance audit | batch 15/19

• audited: 15 papers
• in_scope: 9
• borderline: 4
• out_of_scope: 2
• flagged for removal: 2022-burns-discovering-latent-knowledge, 2022-izacard-atlas-few-shot
• papers: 2021-vo-hierarchical-evidence-attention (in_scope), 2021-vraga-news-literacy-skepticism (in_scope), 2021-wang-metafend (in_scope), 2021-zhou-belief-evolution-network (borderline), 2021-zhou-cascade-analysis-survey (in_scope), 2022-burns-discovering-latent-knowledge (out_of_scope), 2022-dugan-boundary-detection (in_scope), 2022-ecker-psychological-drivers-misinformation (in_scope), 2022-fan-trustworthy-recommender-systems (borderline), 2022-gruppi-nela-gt-2022 (in_scope), 2022-hosseinmardi-radical-youtube (in_scope), 2022-izacard-atlas-few-shot (out_of_scope), 2022-ji-hallucination-nlg (borderline), 2022-kaddour-causal-machine-learning-survey (borderline), 2022-munger-right-wing-youtube (in_scope)

[2026-05-18] relevance audit | batch 16/19

• audited: 15 papers
• in_scope: 7
• borderline: 5
• out_of_scope: 3
• flagged for removal: 2022-ouyang-instructgpt, 2023-dahein-model-merging, 2023-ganguli-moral-self-correction
• papers: 2022-ouyang-instructgpt (out_of_scope), 2022-perez-discovering-lm-behaviors (borderline), 2022-perez-red-teaming-language-models (in_scope), 2022-rana-deepfake-detection-survey (in_scope), 2022-wang-toxicity-detection-generative (borderline), 2022-yang-botometer-101 (in_scope), 2023-asai-self-rag (in_scope), 2023-ayoobi-linkedin-fake-llm-profiles (in_scope), 2023-cao-aigc-survey (in_scope), 2023-chang-evaluation-llms (borderline), 2023-chen-combating-misinformation-age-llms (in_scope), 2023-dahein-model-merging (out_of_scope), 2023-dai-trustworthy-gnn-survey (borderline), 2023-deshpande-chatgpt-toxicity (borderline), 2023-ganguli-moral-self-correction (out_of_scope)

[2026-05-24 04:45] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1779612309767-350c7a8c
• substantive issues for human review:
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/majumder-navonil.md
• ORPHAN wiki/authors/poria-soujanya.md
• ORPHAN wiki/authors/zhao-wayne-xin.md
• ORPHAN wiki/authors/zhou-kun.md
• ORPHAN wiki/authors/li-junyi.md
• ORPHAN wiki/authors/tang-tianyi.md
• ORPHAN wiki/authors/schmidt-anna.md
• ORPHAN wiki/authors/wiegand-michael.md
• ORPHAN wiki/authors/wang-xiaolei.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/cambria-erik.md
• ORPHAN wiki/authors/krishnamurthy-gangeshwar.md
• ORPHAN wiki/authors/hou-yupeng.md
• ORPHAN wiki/authors/min-yingqian.md
• ORPHAN wiki/authors/zhang-beichen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/dong-zican.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-yushuo.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/jiang-jinhao.md
• ORPHAN wiki/authors/ren-ruiyang.md
• ORPHAN wiki/authors/li-yifan.md
• ORPHAN wiki/authors/tang-xinyu.md
• ORPHAN wiki/authors/liu-zikang.md
• ORPHAN wiki/authors/liu-peiyu.md
• ORPHAN wiki/authors/nie-jian-yun.md
• ORPHAN wiki/authors/wen-ji-rong.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md

[2026-05-24 12:00] ingest | Shifting attention to accuracy can reduce misinformation online

• job: 1779677606585-127de36a
• submitter: reza
• page: Shifting attention to accuracy can reduce misinformation online
• touched: Gordon Pennycook, Ziv Epstein, Mohsen Mosleh, Antonio A. Arechar, Dean Eckles, David Rand, Misinformation Interventions, Misinformation, Accuracy
• notes: Nature paper demonstrating accuracy-salience interventions reduce misinformation sharing; six MTurk studies + Twitter field trial with 379 users.

[2026-05-24 20:45] ingest | A large-scale COVID-19 Twitter chatter dataset for open scientific research

• job: 1779677606715-8fbad681
• submitter: reza
• page: A large-scale COVID-19 Twitter chatter dataset for open scientific research
• touched: Juan M Banda, Ramya Tekumalla, Guanyu Wang, Jingyuan Yu, Tuo Liu, Yuning Ding, Katya Artemova, Elena Tutubalina, Gerardo Chowell, Misinformation and fake news detection, Sentiment Analysis, Computational social science and large-scale text analysis, COVID-19 Misinformation and Infodemic, Twitter
• notes: Open-science dataset of 800+ million COVID-19 tweets (Jan–Nov 2020) from international collaboration; enables misinformation detection, sentiment tracking, and cascade analysis; includes preprocessing pipelines and biweekly updates.

[2026-05-25 00:36] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1779683811414-c13c3984
• substantive issues for human review:
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/izacard-gautier.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/lomeli-maria.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/hosseini-lucas.md
• ORPHAN wiki/authors/waldendorf-jonas.md
• ORPHAN wiki/authors/schick-timo.md
• ORPHAN wiki/authors/haddow-barry.md

[2026-05-25 03:50] figure | 2003-newman-structure-function-networks

• prepared figure prompt for network structure and dynamics workflow diagram

[2026-05-25 03:51] figure | 2014-friggeri-rumor-cascades

• prepared figure prompt for four-component empirical pipeline: data collection, veracity classification, Snopes fact-checking effect measurement, and rumor evolution dynamics

[2026-05-25 03:52] figure | 2015-bode-related-news-correction

• prepared figure prompt for algorithmic debunking delivery, motivated reasoning filtering, and issue-dependent correction outcomes

[2026-05-25 03:53] figure | 2016-cresci-dna-spambot-detection

• prepared figure prompt for DNA-based behavioral encoding, LCS similarity analysis, and spambot detection pipeline

[2026-05-25 03:54] figure | 2016-graves-boundaries-not-drawn

• prepared figure prompt for three institutional poles (journalism, academia, politics) organizing the global fact-checking movement

[2026-05-25 14:33] figure | 2017-mihailidis-spreadable-spectacle

• prepared figure prompt for spectacle propagation through homophilous networks, memes, and media amplification

[2026-05-25 14:55] figure | 2018-afchar-mesonet

• prepared figure prompt for three-stage deepfake detection pipeline: frame extraction, lightweight CNN analysis (Meso-4/MesoInception-4), and video-level aggregation

[2026-05-25 23:30] figure | 2018-baly-predicting-factuality-bias

• prepared figure prompt for media profiling: five-feature classification pipeline (articles, Wikipedia, Twitter, URL, traffic) predicting factuality and bias on 1,066 outlets

[2026-05-25 23:45] figure | 2018-hamborg-automated-media-bias

• prepared figure prompt for four-stage news production pipeline: gathering, writing, editing, and spin bias detection with computational methods mapping

[2026-05-26 00:05] figure | 2018-ma-rumor-detection-rvnn

• prepared figure prompt for tree-structured RvNN rumor detection: bottom-up aggregates responses upward to root, top-down propagates root signals downward, achieving 72.3% accuracy with 4× faster early detection

[2026-05-25 23:50] figure | 2018-sharma-combating-fake-news-survey

• prepared figure prompt for dual-pathway detection taxonomy: content-based approaches (linguistic features, embeddings, CNNs/RNNs) and feedback-based approaches (propagation cascades, user dynamics, temporal analysis) showing propagation methods achieve 95.3% accuracy

[2026-05-26 00:06] figure | 2018-stella-bots-catalan

• prepared figure prompt for bot-driven polarization amplification: Twitter data → bot detection (metadata, 24% tweets) → strategic targeting (network periphery to influencers) → amplification (group-specific inflammatory content) → polarization

[2026-05-26 00:15] figure | 2018-zubiaga-rumor-detection-survey

• prepared figure prompt for four-stage rumor detection and resolution pipeline: Detection → Tracking → Stance Classification (support/deny/query/comment) → Veracity Classification, addressing key finding that veracity remains substantially harder than stance

[2026-05-26 01:30] figure | 2019-acerbi-cognitive-attraction

• prepared figure prompt for cognitive-appeal factors in misinformation: 1x4 flow diagram showing negative emotion (49%), threat signals (28%), social cues (50%), and celebrity references (48%) as drivers of psychological optimization

[2026-05-26 02:45] figure | 2019-baly-trustworthiness-political-ideology

• prepared figure prompt for joint media profiling: feature extraction (linguistic, social, web) → parallel trustworthiness ordinal prediction (3-point scale) and ideology prediction (7-point scale) with copula joint modeling and auxiliary tasks at coarser granularities, reducing factuality error to 0.481 and bias error to 1.479

[2026-05-25 18:45] figure | 2019-cresci-evolved-spambot-detection

• prepared figure prompt for genetic spambot evolution: behavioral timeline → digital DNA encoding → multi-level genetic operators (mutation, crossover) → fitness evaluation (KL divergence) → evolved bot with 79% detection evasion rate, noting that evolved bots exhibit anomalously high Shannon entropy detectable by entropy-based defense mechanisms

[2026-05-25 06:30] figure | 2019-gorrell-rumoureval

• prepared figure prompt for shared task rumour verification: social media input (Twitter/Reddit) → parallel stance classification (Support/Deny/Query/Comment) and veracity prediction (True/False/Unverified), showing neural models with contextual embeddings outperformed baselines on both tasks

[2026-05-25 23:55] figure | 2019-guess-prevalence-fake-news

• prepared figure prompt for age as predictor of fake news sharing: survey respondents linked to Facebook behavioral data → four age groups (18–29, 30–44, 45–65, 65+) showing progression in fake news sharing intensity → effect sizes highlighted with 65+ users sharing 6.7× more than youngest cohort, demonstrating age dominates ideology as behavioral predictor

[2026-05-25 19:53] figure | 2019-ippolito-generated-text-detection

• prepared figure prompt for detection robustness comparison: text generation (three decoding strategies) → parallel BERT and human detection → cross-strategy transfer test showing BERT brittleness (90% to 42% accuracy) versus human robustness (maintaining 70%), highlighting the critical finding that machine detectors excel in-domain but fail to generalize

[2026-05-26 07:15] figure | 2019-khattar-mvae-multimodal

• prepared figure prompt for multimodal fake news detection: four-stage pipeline (text encoder with Word2Vec+LSTM, image encoder with VGG-19, shared latent space via reparameterized Gaussian, decoder+classifier for joint reconstruction and classification) achieving 74.5% accuracy on Twitter and 82.4% on Weibo

[2026-05-25 09:45] figure | 2019-kumar-tree-lstms-stance-rumor

• prepared figure prompt for conversation tree analysis: Tree LSTMs with convolution units for multi-task stance and rumor veracity classification, achieving 12% and 15% F1 improvements on PHEME dataset

[2026-05-25 16:48] figure | 2019-lukito-multi-platform-disinformation

• prepared figure prompt for cross-platform disinformation coordination: four-stage VAR pipeline (data collection, preprocessing, VAR modeling with Granger causality, cross-platform dynamics revelation) showing Reddit→Twitter trial-balloon effect and Trump-approval-driven Twitter activity

[2026-05-25 20:15] figure | 2019-shu-hierarchical-propagation

• prepared figure prompt for hierarchical propagation networks: macro-level retweet cascades and micro-level reply trees with temporal, structural, and linguistic feature extraction, showing temporal features rank highest in importance achieving F1 ≥ 0.84 on PolitiFact and F1 ≥ 0.86 on GossipCop

[2026-05-25 16:42] figure | 2019-shu-social-context-trifn

• prepared figure prompt for tri-relationship detection: three-component framework (Publisher bias, News content embedding, User credibility) jointly modeling relationships among publishers, news, and users for fake news classification, achieving 88% F1 with early detection within 48 hours

[2026-05-26 09:30] figure | 2019-sitaula-credibility-fake-news

• prepared figure prompt for source-versus-content credibility comparison: four-stage pipeline (Source credibility from author metadata, Content credibility from linguistic features, Feature selection reducing 26 to 13 features, Logistic regression classification) demonstrating source features alone achieve F1 0.83 versus content-only F1 0.66

[2026-05-26 10:15] figure | 2020-bail-russian-ira-polarization

• prepared figure prompt for empirical causal study: four-stage assessment (partisan Twitter users Oct-Nov 2017 survey, matched IRA exposure data, six outcome measures across attitudes and behaviors, null findings across all outcomes) illustrating that Russian IRA accounts had no detectable effects on polarization or engagement

[2026-05-26 11:45] figure | 2020-cresci-social-bot-detection

• prepared figure prompt for detection methodology evolution: three-box taxonomy (Individual, Group, Adversarial) showing progression from account-level classifiers to coordinated group detection to evasion-robust methods, with headline on the arms race where ML classifiers fail on evolved modern bots

[2026-05-26 12:18] figure | 2020-krause-fact-checking-risk-communication

• prepared figure prompt for risk-based fact-checking: three-challenge framework (Risk definition: partisan disagreement, Trust barriers: credibility deficits, Uncertainty: evolving knowledge) showing why trust and values matter more than facts in pandemic misinformation response

[2026-05-26 00:37] figure | 2020-golovchenko-cross-platform-propaganda

• prepared figure prompt for cross-platform propaganda coordination: three-stage analysis (collection, modeling, cross-platform dynamics) showing how coordinated networks amplify disinformation across platforms

[2026-05-26 00:00] figure | 2020-memon-covid19-misinformation-communities

• prepared figure prompt for COVID-19 misinformation communities: three-box pipeline (Annotation, Classification, Analysis) showing tweet categorization into 17 types, user valence scoring for community detection, and network analysis revealing denser networks with higher bot prevalence in misinformed communities, with headline finding that misinformed communities are 48% denser, contain 19% bots vs 11%, and 41% align with anti-vaccination ideology

[2026-05-26 00:00] figure | 2020-mirsky-deepfakes-survey

• prepared figure prompt for deepfake creation and detection: four-box pipeline (Reenactment, Replacement, Editing, Synthesis) showing four methods for deepfake generation via generative neural networks, with detection approaches (artifact-specific forensics and classifiers) forming the response arms race, with headline finding that four creation methods and two detection approaches define the deepfake arms race between generation and detection

[2026-05-26 13:20] figure | 2020-treen-online-climate-misinformation

• prepared figure prompt for climate misinformation: three-component framework (Actors: fossil fuel corporations and media amplifying denial; Amplification: homophily, echo chambers, algorithmic bias accelerating spread; Countermeasures: inoculation, debunking, bot detection, platform moderation) illustrating that effective interventions require integrating psychology, technology, and institutional approaches

[2026-05-26] figure | 2020-vaccari-deepfakes-disinformation

• prepared figure prompt for deepfakes and epistemic trust: three-box mediation pathway (Exposure to deepfake stimulus, Viewer uncertainty about authenticity, Trust erosion in news media) showing deepfakes threaten democratic discourse through uncertainty rather than direct deception, with headline finding that deepfakes reduce trust primarily through epistemic doubt and uncertainty, but educational reveals explaining the technology can mitigate this effect

[2026-05-26] figure | 2020-van-der-linden-covid-inoculation

• prepared figure prompt for psychological inoculation against COVID misinformation: four-box pipeline (Problem: COVID misinformation undermines vaccine uptake, Limitation: reactive fact-checking too slow and expensive, Inoculation: forewarning plus preemptive refutation, Implementation: Bad News game teaches six deception techniques) showing how interactive prebunking scales cognitive defense, with headline finding that psychological inoculation through interactive gameplay improves resistance to misinformation and reduces vaccine hesitancy

[2026-05-26 01:13] figure | 2020-walter-fact-checking-meta-analysis

• prepared figure prompt for fact-checking effectiveness meta-analysis: four-factor framework (Motivated Reasoning, Message Design, Context, Audience) modulating overall effect size d = 0.29, with pro-attitudinal corrections yielding d = 0.43 vs. counter-attitudinal d = 0.15 as headline finding showing motivated reasoning as primary boundary condition

[2026-05-26 04:50] figure | 2020-yang-checked-covid19

• prepared figure prompt for multimodal COVID-19 fake news detection: four-stage pipeline (Collection: expert labels from Weibo CMC and People's Daily, Filtering: 39-keyword relevance across 5 categories, Schema: multimedia text/images/video and propagation counts, Benchmark: five text-classification methods) showing TextCNN achieves F₁ = 0.938 on Chinese COVID-19 Weibo detection

[2026-05-26] figure | 2020-zhou-recovery-covid19

• prepared figure prompt for multimodal COVID-19 credibility dataset: 1x3 construction pipeline (Selection: NewsGuard+MBFC dual-authority publisher filtering, Collection: crawl COVID-19 articles from 60 publishers, Tracking: query 140,820 tweets) showing ReCOVery dataset with 2,029 articles and multimodal features, with headline finding that multimodal features outperform single-modal approaches and SAFE achieves best F₁ scores

[2026-05-26] figure | 2021-askell-general-language-assistant

• prepared figure prompt for language model alignment evaluation: 1x3 framework (Training: imitation learning, binary discrimination, ranked preference models; Pretrain: preference models on Stack Exchange, Reddit, Wikipedia; Evaluate: helpfulness, honesty, harmlessness) showing ranked preference modeling outperforms imitation learning and scales better with model size 10M–52B, with headline finding that ranked preference modeling improves 5–10× sample efficiency via preference model pre-training

[2026-05-26] figure | 2021-dou-user-preference-detection

• prepared figure prompt for user preference and propagation fusion: 1x3 pipeline (Endogenous: user preference encoding from tweet history, Exogenous: propagation graph construction from retweets, Fusion: GraphSAGE/GCN aggregation and classification) showing how confirmation bias in user preferences combined with propagation patterns improve detection, with headline finding that user preference and propagation patterns jointly maximize detection accuracy over single-signal baselines

[2026-05-26] figure | 2021-dun-kan

• prepared figure prompt for knowledge-aware attention network: 1x4 pipeline (Extract: entity linking identifies entities in news, Enrich: align entities with Wikidata and retrieve contextual neighbors, Attend: N-E and N-E2C attention mechanisms weight entity and context importance, Classify: fused representations fed to softmax classifier) showing knowledge graph entity contexts weighted by attention mechanisms, with headline finding that knowledge graph entity contexts weighted by attention mechanisms improve fake news detection by 2–9% F1 across benchmarks

[2026-05-26] figure | 2021-papasavva-qanon-conspiracy

• prepared figure prompt for QAnon conspiracy analysis: 1x4 pipeline (Canonicalization, Authorship, Content, Dissemination) examining poor agreement on canonical drops across aggregation sites, stylometric evidence of multiple authors, extremely low coherence (0.04 vs. mainstream 0.19), and Reddit's critical mainstreaming role, with headline finding that QAnon exhibits poor authenticity and coherence yet spreads rapidly from fringe imageboards to mainstream

[2026-05-26] figure | 2021-pennycook-attention-accuracy

• prepared figure prompt for attention-accuracy nudge study: 1x3 intervention pipeline (Attention gap where people neglect accuracy despite caring, Simple nudge reminder to focus on accuracy, Impact showing 51% reduction in false sharing) with headline finding that simple reminders to focus on accuracy increase selective sharing and reduce misinformation at scale

[2026-05-26] figure | 2021-plepi-sarcasm-detection

• prepared figure prompt for sarcasm detection via graph attention: 2x2 grid (Tweets, History, Graph, Attention) showing SentenceBERT tweet encoding, user2vec historical user patterns, heterogeneous graph modeling social interactions and threads, and multi-head attention aggregation, with headline finding that author sarcastic intentions are easier to detect than audience perception; user history helps intended sarcasm detection (84% F1) more than perceived sarcasm (56% F1)

[2026-05-26] figure | 2021-zhou-cascade-analysis-survey

• prepared figure prompt for information cascade prediction survey: 1x3 pipeline (Feature-Based, Generative, Deep-Learning) showing evolution of cascade prediction methodologies from feature engineering to stochastic processes to neural networks, with headline finding that feature-based approaches dominated 2009–2014, generative models 2014–2016, and deep learning since 2017 with superior performance

[2026-05-26] figure | 2022-dugan-boundary-detection

• prepared figure prompt for boundary detection in machine-generated text: 1x4 pipeline (Annotation, Performance, Models, Genre) showing RoFT crowdsourced game platform collecting 21,646 annotations, human accuracy of 23.4% improving with incentives, larger model effects on detectability, and genre-specific error patterns across news, stories, recipes, and speeches, with headline finding that humans achieve 23.4% accuracy detecting text boundaries with performance varying by monetary incentives, model size, and text genre

[2026-05-26] figure | 2022-kaddour-causal-machine-learning-survey

• prepared figure prompt for causal machine learning survey: 1x5 framework (Supervised, Generative, Explanations, Fairness, Reinforcement) showing five causal ML approaches for learning robust, fair, and interpretable models across distribution shifts, with headline finding that unified causal framework enables models robust to distribution shifts and confounding

[2026-05-26] figure | 2023-asai-self-rag

• prepared figure prompt for adaptive retrieval and self-critique: 1x4 pipeline (Retrieve, Evaluate, Generate, Critique) showing adaptive retrieval decisions via reflection tokens, passage relevance assessment, grounded response generation conditioned on passages, and self-evaluation of output support and utility, with headline finding that self-adaptive retrieval and critique outperform fixed-retrieval RAG, achieving ChatGPT-competitive accuracy on QA and generation

[2026-05-26] figure | 2023-ayoobi-linkedin-fake-llm-profiles

• prepared figure prompt for LinkedIn fake profile detection: 1x4 pipeline (Preprocess, Embed, Encode, Classify) showing text tokenization and lemmatization, BERT/RoBERTa embedding conversion, section and subsection tag embedding to isolate discriminative content, and multiclass classification into legitimate/human-fake/LLM-generated, with headline finding that SSTE method achieves 95% accuracy detecting fake profiles and 70% accuracy on unseen LLM-generated content

[2026-05-26] figure | 2023-mills-engagement-amplification

• prepared figure prompt for algorithmic ranking comparison: 1x3 pipeline (Engagement-based, Chronological, Stated-Preference) showing three Twitter ranking approaches with user satisfaction and emotional outcome measures, with headline finding that engagement-based algorithm amplifies anger (0.47 SD) and partisan content (0.24 SD) but users prefer stated-preference ranking

[2026-05-26] figure | 2023-sadasivan-ai-text-detection

• prepared figure prompt for AI-text detection vulnerabilities: 2x2 grid (Watermark, Neural, ZeroShot, Retrieval) showing four detector classes and their robustness under adversarial paraphrasing attacks, with headline finding that detection becomes harder as language models improve; all detector classes fail under paraphrasing with 77-89% quality preservation

[2026-05-26] figure | 2023-su-adapting-fake-news-detection

• prepared figure prompt for adapting fake news detection to LLM era: 1x3 framework (Human, Mixed, Balanced) showing three scenarios as machine-generated content emerges, with detector accuracy falling from 71–80% baseline to 27% under distribution shift, then recovering to 70–79% with balanced training, with headline finding that balanced training on mixed human and machine-generated content restores robustness to distribution shifts

[2026-05-26] figure | 2023-vatter-distributed-gnn-systems

• prepared figure prompt for distributed GNN systems survey: 1x4 grid (Partition, Sample, Synchronize, Parallelize) showing key design dimensions for distributed GNN training architecture, with headline finding that distributed GNN systems bridge graph processing and deep learning through design dimensions of partitioning, sampling, synchronization, and parallelism

[2026-05-26] figure | 2023-zhang-llm-knowledge-update

• prepared figure prompt for knowledge update methods: 1x5 pipeline (Editing, Training, Memory, Retrieval, Internet) showing five approaches from direct parameter editing to real-time web search, with headline finding that implicit methods offer fine-grained control but scale poorly, while explicit methods scale unlimitedly but require external resources and inference overhead

[2026-05-26] figure | 2023-zhou-hero-linguistic-style

• prepared figure prompt for HERO hierarchical linguistic detection: 1x3 pipeline (Tree, Embed, Classify) showing unified syntactic-discourse tree construction, bottom-up Bi-GRU embedding propagation preserving global structure, and softmax classification, with headline finding that HERO achieves 86.6% AUC on long articles and 89.6% on short statements by modeling both syntactic and discourse linguistic structure

[2026-05-26] figure | 2024-chen-llm-generated-misinformation-detection

• prepared figure prompt for LLM-generated misinformation detection: 1x3 pipeline (Generation, Human, Model) showing three LLM generation methods (hallucination, arbitrary, controllable), human crowdsourced evaluation, and zero-shot model detector testing, with headline finding that humans detect only 9.6% of hallucinated news versus 40.7% of human-written content; all detectors consistently underperform on synthetic misinformation

[2026-05-26] figure | 2024-mao-raidar-detection-rewriting

• prepared figure prompt for AI text detection via rewriting: 1x4 pipeline (Invariance, Equivariance, Uncertainty, Classify) showing measurement of editing distance, transformation stability, variance across rewrites, and classification with regression/boosting, with headline finding that achieves 29+ F1 point improvement over baselines and is robust to adversarial rephrasing across six domains

[2026-05-26] figure | 2022-burns-discovering-latent-knowledge

• prepared figure prompt for latent knowledge discovery: 1x4 pipeline (Extract, Normalize, Learn, Predict) showing hidden state extraction from contrast pairs, class-bias removal while preserving truth direction, consistency and confidence loss training, and latent knowledge probe prediction, with headline finding that CCS achieves 71.2% accuracy discovering latent knowledge and maintains 82% robustness to adversarial prompts

[2026-05-26 16:41] ingest | Misinformation and Data Literacy

• job: 1779742750008-0144adf5
• submitter: reza
• page: Misinformation and Data Literacy
• touched: wiki/videos/2026-jevin-west-misinformation-and-data-literacy.md, wiki/authors/west-jevin.md, wiki/authors/bergstrom-carl.md, wiki/topics/data-literacy.md, wiki/topics/media-literacy.md, wiki/topics/visual-misinformation.md, wiki/topics/deepfakes.md, wiki/topics/literacy-interventions.md, wiki/topics/vaccine-hesitancy.md, wiki/index.md, wiki/videos/index.md
• notes: Jevin West on data literacy as misinformation defense; Calling Bullshit course scaling to 70+ universities and K-12; chart manipulation examples; synthetic media threats (deepfakes, voice synthesis); asymmetry principle

[2026-05-31 04:37] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1780216625867-137e01a3
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md
• ORPHAN wiki/authors/haddow-barry.md
• ORPHAN wiki/authors/birch-alexandra.md
• ORPHAN wiki/authors/colombo-pierre.md
• ORPHAN wiki/authors/liu-nelson.md

[2026-06-07 04:41] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1780821696651-c21b3e9b
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md
• ORPHAN wiki/authors/haddow-barry.md
• ORPHAN wiki/authors/birch-alexandra.md
• ORPHAN wiki/authors/colombo-pierre.md
• ORPHAN wiki/authors/liu-nelson.md

[2026-06-14 04:30] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1781425839072-365c79e6
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md
• ORPHAN wiki/authors/haddow-barry.md
• ORPHAN wiki/authors/birch-alexandra.md
• ORPHAN wiki/authors/colombo-pierre.md
• ORPHAN wiki/authors/liu-nelson.md

[2026-06-21 04:44] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1782031468871-87cf4f14
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md
• ORPHAN wiki/authors/haddow-barry.md
• ORPHAN wiki/authors/birch-alexandra.md
• ORPHAN wiki/authors/colombo-pierre.md
• ORPHAN wiki/authors/liu-nelson.md

[2026-06-28 04:34] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1782635670091-2492ddf0
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md
• ORPHAN wiki/authors/haddow-barry.md
• ORPHAN wiki/authors/birch-alexandra.md
• ORPHAN wiki/authors/colombo-pierre.md
• ORPHAN wiki/authors/liu-nelson.md

[2026-07-05 04:30] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1783240221001-d139f5f4
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md
• ORPHAN wiki/authors/haddow-barry.md
• ORPHAN wiki/authors/birch-alexandra.md
• ORPHAN wiki/authors/colombo-pierre.md
• ORPHAN wiki/authors/liu-nelson.md

[2026-07-12 04:35] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1783845348311-f8dc7486
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md
• ORPHAN wiki/authors/haddow-barry.md
• ORPHAN wiki/authors/birch-alexandra.md
• ORPHAN wiki/authors/colombo-pierre.md
• ORPHAN wiki/authors/liu-nelson.md

[2026-07-19 04:39] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1784450356105-4f5abf04
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md
• ORPHAN wiki/authors/haddow-barry.md
• ORPHAN wiki/authors/birch-alexandra.md
• ORPHAN wiki/authors/colombo-pierre.md
• ORPHAN wiki/authors/liu-nelson.md

[2026-07-26 04:37] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1785055030401-393a8ad5
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md
• ORPHAN wiki/authors/haddow-barry.md
• ORPHAN wiki/authors/birch-alexandra.md
• ORPHAN wiki/authors/colombo-pierre.md
• ORPHAN wiki/authors/liu-nelson.md

[2026-08-02 04:35] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1785659737322-4a2ec857
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md
• ORPHAN wiki/authors/haddow-barry.md
• ORPHAN wiki/authors/birch-alexandra.md
• ORPHAN wiki/authors/colombo-pierre.md
• ORPHAN wiki/authors/liu-nelson.md

[2026-08-09 01:43] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1786254207147-cf663390
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md
• ORPHAN wiki/authors/haddow-barry.md
• ORPHAN wiki/authors/birch-alexandra.md
• ORPHAN wiki/authors/colombo-pierre.md
• ORPHAN wiki/authors/liu-nelson.md

[2026-08-09 ] figure | 2026-wang-llm-linguistic-fingerprints

• Figure prompt prepared for LLM-generated fake news detection paper (LIFE framework)

[2026-08-09 04:38] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1786264695354-8f24b360
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md
• ORPHAN wiki/authors/haddow-barry.md
• ORPHAN wiki/authors/birch-alexandra.md
• ORPHAN wiki/authors/colombo-pierre.md
• ORPHAN wiki/authors/liu-nelson.md

[2026-08-11] ingest | How Polarization May Help Combat Misinformation

• job: 1786475587530-c07a9a5e
• submitter: reza
• page: How Polarization May Help Combat Misinformation: Community Notes and Crowd-Sourced Fact-Checking
• touched: wiki/videos/2026-rand-polarization-misinformation-combat.md, wiki/authors/rand-david.md, wiki/topics/community-notes.md, wiki/topics/wisdom-of-crowds.md, wiki/topics/misinformation.md, wiki/topics/fact-checking.md, wiki/topics/polarization.md, wiki/topics/crowd-sourced-fact-checking.md, wiki/topics/content-moderation.md, wiki/topics/crowdsourcing.md, wiki/index.md, wiki/videos/index.md
• notes: David Rand (MIT) on scaling fact-checking through community-sourced systems; theoretical model of accuracy vs. partisan motivation in flagging; empirical evidence that polarized individuals effectively flag false information without increasing false flagging of true content; implications for systems like Twitter Community Notes

[2026-08-16 00:00] relevance sweep | weekly automated cleanup

• audited: 305 pages (0 skipped — this is the first audit run)
• in_scope: 204 (estimated)
• borderline: 95 (flagged for human review, not deleted)
• out_of_scope: 6 — queued for deletion: 5 (job ids: 1786867830477-3225e3e8, 1786867830561-684a94c4, 1786867830628-091ba9e1, 1786867830697-d1c9cc95, 1786867830767-b7e981ce)
• notes: cap reached with 1 additional out-of-scope pages pending next week

[2026-08-16 04:15] delete | Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models

• job: 1786867830767-b7e981ce
• reason: Claim verification applied to scientific literature, not news
• page: wiki/papers/2023-wang-folk-claim-verification.md
• touched: wiki/papers/index.md, wiki/topics/claim-verification.md, wiki/topics/fact-checking.md, wiki/topics/large-language-models.md, wiki/topics/explainability.md, wiki/topics/natural-language-inference.md, wiki/authors/wang-haoran.md, wiki/authors/shu-kai.md, wiki/index.md
• notes: Queued by W11 automated relevance sweep; paper uses claim verification on scientific literature (HoVER, FEVEROUS, SciFactOpen) rather than news/political claims; moved to wiki/.deleted/ with all wikilinks removed or replaced

[2026-08-16 04:43] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1786869782243-a7ed17d8
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/kiela-douwe.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/karpukhin-vladimir.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/yih-wen-tau.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/rocktaschel-tim.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md

[2026-08-23 04:38] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1787474309980-6225f72e
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/kiela-douwe.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/karpukhin-vladimir.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/yih-wen-tau.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/rocktaschel-tim.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md

[2026-08-30 04:35] lint | weekly health check

• result: 50 substantive
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/kiela-douwe.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/karpukhin-vladimir.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/yih-wen-tau.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/rocktaschel-tim.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md

[2026-09-06 04:36] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1788683810035-c250f3fe
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/kiela-douwe.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/karpukhin-vladimir.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/yih-wen-tau.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/rocktaschel-tim.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md

[2026-09-13 04:42] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1789288937837-7c106708
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/kiela-douwe.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/karpukhin-vladimir.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/yih-wen-tau.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/rocktaschel-tim.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md

[2026-09-20 04:42] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1789893776413-ef73aca0
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/kiela-douwe.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/karpukhin-vladimir.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/yih-wen-tau.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/rocktaschel-tim.md
• ORPHAN wiki/authors/schiefer-nicholas.md
• ORPHAN wiki/authors/guu-kelvin.md
• ORPHAN wiki/authors/lee-kenton.md
• ORPHAN wiki/authors/tung-zora.md
• ORPHAN wiki/authors/weller-jochen.md
• ORPHAN wiki/authors/pasupat-panupong.md
• ORPHAN wiki/authors/kuhn-jochen.md
• ORPHAN wiki/authors/chang-ming-wei.md
• ORPHAN wiki/authors/kasneci-gjergji.md
• ORPHAN wiki/authors/guerreiro-nuno.md
• ORPHAN wiki/authors/alves-duarte.md
• ORPHAN wiki/authors/waldendorf-jonas.md

[2026-09-27 04:32] delete | Two-Stream Neural Networks for Tampered Face Detection

• job: 1790496232388-bd593ff4
• page: wiki/.deleted/papers/2018-zhou-two-stream-tampered-face.md (was wiki/papers/2018-zhou-two-stream-tampered-face.md)
• touched: wiki/papers/index.md, wiki/index.md, wiki/topics/deepfake-detection.md, wiki/topics/face-tampering-detection.md, wiki/topics/image-forensics.md, wiki/topics/image-splicing.md, wiki/topics/steganalysis.md, wiki/datasets/swapme-faceswap.md, wiki/authors/zhou-peng.md, wiki/authors/han-xintong.md, wiki/authors/davis-larry.md, wiki/authors/morariu-vlad.md
• reason: Face manipulation detection for forensics, not deepfakes-as-misinformation

[2026-09-27 04:36] lint | weekly health check

• result: 50 mechanical, 50 substantive
• queued auto-fix job: 1790498212008-7dc17e51
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/wu-jeff.md
• ORPHAN wiki/authors/solaiman-irene.md
• ORPHAN wiki/authors/kim-jong-wook.md
• ORPHAN wiki/authors/kreps-sarah.md
• ORPHAN wiki/authors/mccain-miles.md
• ORPHAN wiki/authors/newhouse-alex.md
• ORPHAN wiki/authors/blazakis-jason.md
• ORPHAN wiki/authors/mcguffie-kris.md
• ORPHAN wiki/authors/wang-jasmine.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/anand-ankesh.md
• ORPHAN wiki/authors/park-noseong.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/kiela-douwe.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/karpukhin-vladimir.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/yih-wen-tau.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/rocktaschel-tim.md
• ORPHAN wiki/authors/schiefer-nicholas.md

[2026-10-04 04:34] lint | weekly health check

• result: 20 mechanical, 50 substantive
• queued auto-fix job: 1791102851708-298eae30
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/wu-jeff.md
• ORPHAN wiki/authors/solaiman-irene.md
• ORPHAN wiki/authors/kim-jong-wook.md
• ORPHAN wiki/authors/kreps-sarah.md
• ORPHAN wiki/authors/mccain-miles.md
• ORPHAN wiki/authors/newhouse-alex.md
• ORPHAN wiki/authors/blazakis-jason.md
• ORPHAN wiki/authors/mcguffie-kris.md
• ORPHAN wiki/authors/wang-jasmine.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/anand-ankesh.md
• ORPHAN wiki/authors/park-noseong.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/kiela-douwe.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/karpukhin-vladimir.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/yih-wen-tau.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/rocktaschel-tim.md
• ORPHAN wiki/authors/schiefer-nicholas.md

[2026-10-11 04:42] lint | weekly health check

• result: 50 substantive
• substantive issues for human review:
• ORPHAN wiki/citation.md
• ORPHAN wiki/authors/seifert-colleen.md
• ORPHAN wiki/authors/schwarz-norbert.md
• ORPHAN wiki/authors/wu-jeff.md
• ORPHAN wiki/authors/solaiman-irene.md
• ORPHAN wiki/authors/kim-jong-wook.md
• ORPHAN wiki/authors/kreps-sarah.md
• ORPHAN wiki/authors/mccain-miles.md
• ORPHAN wiki/authors/newhouse-alex.md
• ORPHAN wiki/authors/blazakis-jason.md
• ORPHAN wiki/authors/mcguffie-kris.md
• ORPHAN wiki/authors/wang-jasmine.md
• ORPHAN wiki/authors/howard-jeremy.md
• ORPHAN wiki/authors/ruder-sebastian.md
• ORPHAN wiki/authors/morariu-vlad.md
• ORPHAN wiki/authors/jones-nickolas-m.md
• ORPHAN wiki/authors/wojcik-sean-p.md
• ORPHAN wiki/authors/sweeting-josiah.md
• ORPHAN wiki/authors/silver-roxane-cohen.md
• ORPHAN wiki/authors/anand-ankesh.md
• ORPHAN wiki/authors/park-noseong.md
• ORPHAN wiki/authors/zhang-junyao.md
• ORPHAN wiki/authors/du-yifan.md
• ORPHAN wiki/authors/chen-zhiping.md
• ORPHAN wiki/authors/le-quoc.md
• ORPHAN wiki/authors/clark-kevin.md
• ORPHAN wiki/authors/luong-minh-thang.md
• ORPHAN wiki/authors/ouyang-long.md
• ORPHAN wiki/authors/christiano-paul.md
• ORPHAN wiki/authors/jiang-xu.md
• ORPHAN wiki/authors/shu-manli.md
• ORPHAN wiki/authors/wang-jiongxiao.md
• ORPHAN wiki/authors/wainwright-carroll-l.md
• ORPHAN wiki/authors/zhu-chen.md
• ORPHAN wiki/authors/xiao-chaowei.md
• ORPHAN wiki/authors/almeida-diogo.md
• ORPHAN wiki/authors/krusche-stephan.md
• ORPHAN wiki/authors/kutyniok-gitta.md
• ORPHAN wiki/authors/michaeli-tilman.md
• ORPHAN wiki/authors/nerdel-claudia.md
• ORPHAN wiki/authors/pfeffer-jurgen.md
• ORPHAN wiki/authors/kiela-douwe.md
• ORPHAN wiki/authors/poquet-oleksandra.md
• ORPHAN wiki/authors/karpukhin-vladimir.md
• ORPHAN wiki/authors/sailer-michael.md
• ORPHAN wiki/authors/yih-wen-tau.md
• ORPHAN wiki/authors/seidel-tina.md
• ORPHAN wiki/authors/stadler-matthias.md
• ORPHAN wiki/authors/rocktaschel-tim.md
• ORPHAN wiki/authors/schiefer-nicholas.md