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A curated reference for researchers working on fake news, misinformation, and disinformation — covering detection methods, propagation dynamics, and the underlying psychology and social science.

Maintained by the Syracuse University DataLab. Every claim on every page traces back to a primary source.

325 Papers 5 Articles 1,311 Authors 866 Topics 26 Datasets 0 Tools 18 Videos

Updated 2026-10-18 · Cite this guide

Survey

Entry points for the field: - Thibault et al. (2025) — A Guide to Misinformation Detection Data and Evaluation in KDD '25 — Comprehensive survey of 75 misinformation detection datasets with systematic quality assessment framework evaluating spurious keyword correlations, temporal correlations, and feasibility of veracity assessment. Introduces CDL-MD unified collection and Evaluation Quality Assurance (EQA) framework proposing that evaluation methodology itself must be rigorously validated alongside algorithm design. Key finding: many widely-used datasets contain spurious correlations allowing artificially inflated performance without robust content understanding; over 50% of claims lack sufficient evidence for credible veracity evaluation. Essential reading for anyone designing detection research. - Loth, Kappes & Pahl (2026) — Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems — Updated perspective on generative AI and misinformation research (2024–2026); introduces methodological tools JudgeGPT (human perception evaluation) and RogueGPT (controlled stimulus generation) for measuring deceptive potential and susceptibility under controlled conditions. Documents agentic shift toward autonomous multi-agent campaigns, multimodal misinformation combining text-image-audio-video, and systemic risks to digital ecosystems (epistemic fragmentation, synthetic consensus manufacturing). Synthesizes mitigation strategies including detection, inoculation/prebunking, provenance infrastructure (C2PA), and platform design interventions. - Modzelewski et al. (2026) — MALicious INTent Dataset and Inoculating LLMs for Enhanced Disinformation Detection — Introduces MALINT, the first English-annotated corpus with 1,600 articles labeled for both credibility and five types of malicious intent (undermining institutions, changing political views, undermining international organizations, promoting social stereotypes, promoting anti-scientific views). Evaluates 12 language models on intent classification; proposes Intent-Based Inoculation (IBI), an intent-augmented reasoning method that improves zero-shot LLM disinformation detection by ~9% on MALINT and ~20% on five diverse datasets across six languages (German, French, Polish, Estonian, Russian, Spanish). - Ai et al. (2026) — The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection — Systematic survey documenting the role of LVLMs in multimodal fake news detection; proposes three-branch taxonomy (parameter-frozen, parameter-tuning, reasoning paradigms) reflecting resource trade-offs; introduces five-dimensional framework for multimodal deception (media authenticity, consistency, source credibility, intent, temporal context); benchmarks 15+ LVLM architectures across eight datasets; identifies eight critical research directions including causal reasoning, adversarial robustness, and knowledge-enhanced adaptation. - Raza et al. (2025) — VLDBench: Evaluating Multimodal Disinformation with Regulatory Alignment — Large-scale human-verified benchmark for multimodal disinformation detection: 31,339 news articles paired with images, curated from 58 outlets across 13 categories. Comprehensive evaluation of 9 LLMs and 10 VLMs reveals vision-language models substantially outperform text-only baselines (e.g., LLaMA-3.2-11B-Vision: 74.82% vs. LLaMA-3.2-1B: 70.29%), yet show critical vulnerability to cross-modal attacks combining text and image manipulation. Explicitly aligned with AI governance frameworks (MIT Risk Repository) to operationalize policy-relevant risk dimensions for responsible deployment. - Cao et al. (2023) — A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT in J. ACM — foundational survey of generative AI covering history (GANs to transformers), technical foundations, unimodal and multimodal models, applications across domains, and critical trustworthiness concerns (factuality, security, privacy, fairness); essential context for understanding AI-enabled misinformation generation and detection. - Chang et al. (2023) — A Survey on Evaluation of Large Language Models in J. ACM — comprehensive survey on evaluation methodologies for LLMs across three dimensions (what, where, how to evaluate); encompasses 269 papers on natural language understanding, generation, reasoning, robustness, ethics, bias, factuality, trustworthiness, and domain-specific applications; essential for understanding how to assess LLMs used in misinformation detection and generation. - Liu et al. (2023) — Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models' Alignment — comprehensive survey on LLM trustworthiness across seven dimensions (reliability, safety, fairness, resistance to misuse, explainability, social norms, robustness) with 29 sub-categories; presents taxonomy, measurement studies on multiple LLMs, and case studies demonstrating effectiveness of alignment varies significantly across trustworthiness categories. - Hamborg, Donnay & Gipp (2018) — Automated identification of media bias in news articles: an interdisciplinary literature review in International Journal on Digital Libraries — bridges social science and computer science research on media bias detection; defines nine bias forms (event/source selection, labeling, placement, spin); maps manual analysis methods to computational approaches - Zhou, Xu, Trajcevski & Zhang (2021) — A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances in ACM Computing Surveys — comprehensive survey of cascade prediction covering 250+ papers; taxonomy of feature-based, generative, and deep learning approaches - Zhou & Zafarani (2020) — A Survey of Fake News in ACM Computing Surveys — concise, authoritative survey of detection methods and opportunities - Shu et al. (2020) — Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements — comprehensive book chapter covering user engagement, weak supervision approaches, and trending issues

Recently added (2026)

• Huang et al. — VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation — Multi-dimensional benchmark evaluating how difficult it is to verify LLM-generated false content; finds that high elicitation success doesn't predict low verification complexity, and generated misinformation can be 3×–169× more expensive to verify than human-written content—a critical asymmetry for fact-checking systems.
• Tsagkarakis, Papadogiannakis & Markatos — Birds of a Feather Flock Together: Network-Based Detection of Coordinated Disinformation Campaigns on Telegram — Graph-based methodology for discovering coordinated disinformation campaigns on Telegram's broadcast channels using only message-sharing patterns, without content analysis. Identified 55 disinformation channels from 18 seed channels; discovered over 86,000 identical message chains replicated across multiple channels, with synchronized posting patterns aligned to major political events. Demonstrates echo-chamber coordination where core channels amplify propaganda while leveraging YouTube and fake-news websites for cross-platform reach.
• Sánchez-Cortés, Monti & De Francisci Morales — Conspiracy and Environment Communities on Reddit Sort Along Different Demographic Axes — Stratified network analysis of how demographic groups (gender, age, affluence, partisanship) enter conspiracy and environmental communities on Reddit through distinct information pathways. Conspiracy communities exhibit upstream sorting: users self-segregate before entry through ideologically-aligned media (masculine users via right-wing outlets; feminine users via esoteric/spiritual content). Environmental communities exhibit downstream sorting: diverse groups converge on the issue but diverge internally by age/affluence on engagement strategy. Reveals that demographic polarization operates through different mechanisms depending on whether communities are identity-expressive or issue-oriented.
• AI-Enabled Human Memory Manipulation: Misleading AI-Generated Summaries Distort — Large language models frequently generate erroneous summaries (100% of summaries contained omissions), and misleading AI summaries distort human memory via the classic misinformation effect: only 48.8% of participants correctly recalled a traffic sign after reading a misleading AI summary vs. 83.6% with accurate information. Critically, susceptibility was equivalent regardless of whether participants believed the summary was AI- or human-written.
• Two Fault Lines: Latent Polarity Geometry in X Community Notes — Analysis of X's Community Notes crowdsourced fact-checking system reveals disagreement is two-dimensional, not one. The platform's one-dimensional bridging algorithm fails on topics with topic-specific disagreement (COVID/vaccines, Ukraine/Russia, institutional trust), causing publication rates to fall from 71.5% to 11.7%. Also documents language-equity gaps: Greek-script notes accumulate fewer ratings and have lower publication rates than global averages.
• Setting the clock: Evaluating temporal window parameters for coordinated behavior — Systematic evaluation of temporal window parameters for coordinated behavior detection in co-repost networks. Finds that window length is a central modeling parameter: short windows (seconds–minutes) isolate highly synchronized coordination; longer windows (hours–days) reveal slower, diffuse coordination. Window stride has negligible effect. Evaluation on 14 labeled information operation datasets demonstrates temporal windowing is a substantive methodological choice, not an implementation detail.
• Drowning in AI Slop: How Social Media Platforms (Do Not) Label AI and Deepfake Content under EU law — Empirical audit of platform compliance with EU Digital Services Act requirements for labeling AI-generated and deepfake content. Tested 10 generative AI tools across Instagram, TikTok, X, and YouTube; found platforms label only 36 of 59 uploads (61%), with highly variable rates: Instagram 82%, TikTok 59%, X 41%, YouTube 63%. Systematic gaps in high-risk content (elections, civic discourse, security).
• Kekatos et al. (2026) — SoK: Formal Methods for Fact-Checking and Information Integrity — Systemization of knowledge surveying 121 works on formal verification techniques (logic, SMT, model checking, theorem proving) applied to fact-checking. Proposes five-level assurance stack (claim, reasoning, system, ecosystem, governance) organizing where formal methods apply. Identifies two critical gaps: translating natural-language claims into machine-checkable form (S4, Construct) and formally updating verdicts when errors are discovered (S8, Correct). Discusses why end-to-end pipeline verification remains unsolved and why fact-checking systems rarely discharge formal warranties of correctness.
• VietPrism: A Large-Scale Vietnamese Speech and Deepfake Corpus with Diverse — Large-scale Vietnamese speech dataset combining 993.4 hours of bona fide speech from 1,262 speakers with 3.1K hours of synthetically generated audio from four systems. Uniquely provides transcripts, consistent speaker identities, five dialect groups, and naturally occurring Vietnamese–English code-switching (~47% of corpus). Zero-shot evaluation of five multilingual deepfake detectors reveals striking brittleness: DFA-1B EER increases from 16.3% to 33.6% as speaker similarity increases; detector performance varies dramatically across generator–detector pairings and exhibits model-dependent disparities across dialects. Establishes benchmark for trustworthy audio-deepfake detection in multilingual, low-resource settings.
• To Trust or Not to Trust: Retrieval-Augmented Fact Checking in Speech — Introduces VeriSpeak, a benchmark of 3,879 spoken claims spanning temporal, geographical, and relational facts. Reveals Large Audio Language Models exhibit a consistent text-speech modality gap (13–25 point accuracy drops) despite strong text-only performance. Demonstrates that transcript-based retrieval-augmented verification with chain-of-thought reasoning reaches 86.1% accuracy, highlighting that speech fact-checking requires both speech understanding and grounded reasoning over evidence.
• Strategic Opinion Manipulation in Multiplex Networks — Game-theoretic model of strategic misreporting on multiplex networks; agents hold private opinions but choose reports strategically per layer, and platforms merge layers into consensus with attention weights. Shows equilibrium consensus reweighted by "tilted centrality" combining network centrality and manipulability. Key finding: whether merging layers helps depends on the square in the manipulability index—merging dilutes manipulation by default but can amplify distortion through re-centralization when agents concentrate extreme opinions on specific layers. Provides closed-form solutions and characterizes platform's optimal attention weights.
• Why Does Misinformation Propagate Faster? An Algorithmic Perspective on X — Identifies the "engagement fungibility mechanism" in X's score-aggregation layer enabling faster misinformation propagation: fast-engagement signals (likes, retweets) and slow-engagement signals (replies, quotes) treated as interchangeable evidence of quality, allowing low-credibility content to gain amplification advantages. Proposes reflective-threshold gate achieving 8–37% credibility-gap reduction without sacrificing overall engagement; validated across 46 robustness checks.
• Making AI-Assisted Claims Independently Challengeable: Publication Authority and a Protocol for Falsifiable Publication Records — Publication Authority protocol ensuring AI-assisted claims are independently verifiable and challengeable; introduces PAC-2026 SF-4 specification formalizing six coordinates (evidence, authorization, measurement, lifecycle, surface correspondence) and five design laws (accountability closure, authorization freshness, epistemic non-amplification, semantic surface preservation, continuing challengeability); evaluates against 110,764 reachable states and 76 unsafe configurations using design science methodology
• Control-Theoretic Content Moderation — Control-theoretic framework for platform-level content moderation; formulates moderation as feedback control with multi-objective loss (harmfulness, activity, engagement); compares fixed-rule, proportional, and control-theoretic strategies (MPC, PID); shows adaptive approaches achieve better balance and faster recovery from shocks
• How neighbourhood ideology shapes misinformation belief in densely tied social networks — Agent-based contagion model integrating individuals' ideological intensity with claims' ideological alignment, showing that network clustering amplifies the influence of neighbours' ideology on misinformation belief beyond individual predisposition. Simulations across multiple topologies (Erdős-Rényi, Watts-Strogatz, Barabási-Albert, Facebook) and extremist placement strategies reveal that in densely clustered networks, local peer pressure can match personal ideological bias in driving belief adoption; even small populations of ideological extremists significantly increase misinformation adoption among moderates in such networks.
• A Multi-Stage Agentic Framework for Effective Counter-Narrative Generation and Refinement — Multi-stage agent-based framework for generating and refining counter-narratives to pro-Russian propaganda about Ukraine. Multi-agent system (Generator, Evaluator, Mediator) uses iterative feedback loops and LLM-as-judge scoring to optimize narratives for persuasiveness, emotional engagement, and shareability. Human validation confirms quality improvements; simulated experiments show refined CNs significantly reduce perceived persuasiveness of target propaganda while maintaining safety parity with expert-curated baselines.
• MMMMM: A Unified Taxonomy for Investigating the Mechanisms of Multilingual MultiModal Misinformation — 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 instances across seven languages. Operationalizes taxonomy via Vision Language Model annotation pipeline (Qwen 3.5 27B) achieving 79.3% accuracy. Reveals cross-linguistic patterns: Portuguese-language misinformation overuses Decorative mechanisms; Japanese predominantly employs Slanted mechanisms (scientific errors and conspiracies); AI-generated images prevalent in technology/science; vaccination misinformation relies on text-based claims.
• Social bots weaken activist cohesion — Longitudinal network analysis of Black Lives Matter protests (June 2020) showing bot infiltration erodes human cohesion in activist networks through structural disruption; effects concentrated among movement supporters at both ego-network and community levels; demonstrates threats to digital civic life operate through relational infrastructure, not just messaging.
• One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spread — Extends Bayesian cascade models to analyze how platform-deployed AI credibility indicators reshape sequential user judgments and public consensus; introduces the Gateway condition demarcating when AI predictions dominate cascade direction; empirical calibration with human-subject experiments reveals heterogeneous user weights on AI signals; simulations show over-reliance on weak AI is harmful while diversification and expert review can improve outcomes.
• A Multi-Branch Feature Fusion Approach for Health Misinformation Detection and Propagation — Multi-branch neural architecture grounded in Elaboration Likelihood Model and Theory of Planned Behaviour for detecting health misinformation and predicting propagation; achieves ROC-AUC of 0.9999 on COVID-19-FNIR and introduces Cognitive Propagation Score, an engagement-independent interpretable metric for prioritizing content when engagement data is unavailable.
• CoVer: Conflict-Aware Claim Verification — Handles evidence-level and aggregation-level conflicts in social media fact-checking via three-stage pipeline (evidence normalization, factual consensus, support verification); ContraNote dataset from X Community Notes; 86–88.5% accuracy outperforming SOTA baselines
• Calibrating Small Language Models for Claim Check-Worthiness Detection — Post-hoc calibration (NN-PPI) for small language models on claim check-worthiness detection; applies nearest-neighbor residual correction to recover large LM accuracy from small models (270M–4B parameters); achieves 12–33.80% weighted F1 gains, enabling cost-effective fact-checking at scale
• Swin Meets EfficientNet: Lightweight Architectures for GAN-Based Face Forensics — Hybrid EfficientNet-B0 with Swin-Transformer for efficient GAN-based synthetic face detection; achieves 99% accuracy and 99.44% recall with practical computational constraints suitable for standard hardware deployment.
• Counter-GEO-Bench: Evaluating Defenses Against Information-Distorting Generative Engine Optimization — Defense benchmark for information-distorting generative engine optimization attacks on LLMs; proposes C-GEO Guard contrastive detector
• Lazy Grounding: Attacking Search Agents with Factual Evidence — Demonstrates "lazy grounding" attacks on retrieval-augmented search agents: injecting factually correct evidence supporting a neighboring question causes agents to misapply this evidence to answer the original question, achieving accuracy drops of 5.9 points on average (up to 17.3 points) without using false or malicious content.
• Unifying Models of Intergroup Hostility in Online Discourse — Unifies six mechanisms of group-directed hostile rhetoric (boundary construction, threat construction, scapegoating, negative evaluation, dehumanization, action orientation) across competing theories; empirical analysis of 2.86M posts from 2024 U.S. election shows threat-mediated and moral-exclusion pathways are complementary, with regular temporal organization from boundary construction through action orientation appearing last.

Sources by type

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New topics

• Human Memory — The cognitive systems by which humans encode, store, and retrieve information; central to understanding how misinformation exploits memory's reconstructive vulnerabilities.
• Misinformation Effect — Classic cognitive psychology phenomenon where exposure to misleading post-event information alters memory for the original event; demonstrated in AI-generated misinformation contexts.
• Algorithmic bias in content systems — Systematic disparities in how algorithms recommend, moderate, rank, or amplify content, including language bias, participation bias, topic bias, and demographic bias in misinformation systems.
• Audio Deepfakes — Detection and analysis of AI-generated or manipulated speech audio, including voice cloning and speech synthesis detection; critical for multilingual and low-resource language scenarios.
• Crowdsourced Fact-Checking — Systems like X's Community Notes that delegate identification and correction of misinformation to crowds of platform users, facing challenges in quality control, disagreement management, and language equity.
• Speech-Based Fact Verification — Automated verification of claims made in spoken language, combining speech understanding with grounded reasoning over evidence sources.
• Formal Methods for Fact-Checking — Application of formal verification techniques (logic, SMT, model checking, theorem proving) to fact-checking, addressing the gap between natural language claims and machine-checkable logical forms.
• Claim Detection — Methods for identifying and extracting claims from text that are subject to fact-checking verification.
• Fact-Checking Systems — End-to-end systems combining claim detection, evidence retrieval, and verdict generation for automated fact verification.
• Explanation in Fact-Checking — Generation of human-interpretable explanations and justifications for fact-checking verdicts, essential for transparency and user trust.
• Recommendation Systems — Algorithmic systems for recommending or ranking content to users; related to misinformation amplification and platform-driven information exposure.
• Engagement-Based Ranking — Ranking algorithms that prioritize content by user engagement metrics, creating incentives that can amplify both true and false information.
• Misinformation Campaigns — Coordinated efforts to spread false information at scale, typically involving multiple actors, platforms, and distribution strategies.
• Telegram — Messaging platform and its role in misinformation spread, coordinated disinformation campaigns, and information ecosystems.

New authors

Massimo Franceschetti, Pan Li, Shuang Gao, Georgios Panayiotou, Debajyoti Mazumder, Mamta, Lorenzo Mannocci, Abhirama Penamakuri, Minh Hoang, Thai Le, Nikolaos Kekatos, Andreas Andreou, Theodoros Nestoridis, Mattea Sim, Charalampos Bratsas, Yael Eiger, Charalampos Dimoulas, Tadayoshi Kohno, Georgios Konstantinidis, Georgios Malogiannis, Andreas Veglis, Raman Ebrahimi, Emmanouil Papadogiannakis, Panteleimon Tsagkarakis, Evangelos Markatos, Hansun Huang, Yutao Wu, Qizhou Wang, Xiang Zheng, Silvia Montaña-Niño, Yige Li, Elif Buse Doyuran, Phoebe Mutch, Xiao Liu, Xingjun Ma, Sarah Erfani, Miguel A. Sánchez-Cortés, Corrado Monti

Foundations

• Kaddour et al. (2022) — Causal Machine Learning: A Survey and Open Problems — comprehensive 191-page survey of CausalML methods that formalize data generation as a structural causal model to enable reasoning about interventions and counterfactuals; taxonomizes 5 problem areas (causal supervised learning, generative modeling, explanations, fairness, reinforcement learning) with systematic comparison of methods and applications to computer vision, NLP, and graph learning; addresses robustness via invariant features, fairness via counterfactual constraints, and generalization across environments.
• Brundage et al. (2018) — The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation — comprehensive threat analysis of AI-enabled attacks across digital, physical, and political security domains; identifies AI-specific threats including deepfakes, automated disinformation campaigns, and denial-of-information attacks; policy recommendations for mitigating malicious AI uses while enabling beneficial applications.
• Bommasani et al. (2021) — On the Opportunities and Risks of Foundation Models — comprehensive 214-page Stanford CRFM report analyzing foundation models (large models trained on broad data and adapted to diverse tasks); examines capabilities across language, vision, and reasoning; applications in healthcare, law, education; and critical societal risks including misuse for misinformation/deepfakes, fairness harms, environmental costs, and security vulnerabilities; proposes frameworks for detection and mitigation.
• Distributed Representations of Words and Phrases and their Compositionality — Extends Skip-gram with phrase representations, negative sampling, and subsampling of frequent words; demonstrates that word vectors exhibit compositional structure via simple vector addition; achieves 72% accuracy on phrase analogy tasks, enabling embedding-based representation of multi-word units and idioms.
• Vaswani et al. (2017) — Attention Is All You Need — introduces the Transformer architecture based entirely on self-attention mechanisms, replacing recurrence and convolution; proposes scaled dot-product and multi-head attention; achieves state-of-the-art BLEU scores on machine translation (28.4 on WMT14 En-De, 41.0 on En-Fr) with significantly faster training; demonstrates strong generalization to parsing and other NLP tasks; canonical architecture underlying BERT, GPT, and virtually all modern fake news detection models.
• Liu et al. (2019) — RoBERTa: A Robustly Optimized BERT Pretraining Approach — systematic replication study showing BERT was undertrained; proposes RoBERTa with four key improvements: dynamic masking, removal of next-sentence-prediction loss, training on longer sequences, and larger mini-batches; achieves state-of-the-art on GLUE, RACE, and SQuAD benchmarks; became foundational pretrained language model for downstream fine-tuning across NLP applications including fake-news and misinformation detection systems.
• Izacard et al. (2022) — Atlas: Few-shot Learning with Retrieval Augmented Language Models — jointly-trained retrieval-augmented model achieving strong few-shot performance on knowledge-intensive tasks with compact parameters; achieves 42.4% on NaturalQuestions and 80.1% on FEVER fact-checking with full data; demonstrates improved parameter efficiency and interpretability compared to dense-only language models; foundational for resource-efficient fact-checking and misinformation detection systems.
• Waseem (2016) — Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter — empirical comparison of expert (feminist and anti-racism activists) vs. crowdsourced annotations on 6,909 tweets; demonstrates that systems trained on expert labels substantially outperform those on crowdworker data (F1 91.19 vs. 83.88); introduces intersectional annotation scheme capturing both racism and sexism simultaneously; foundational methodological work on data quality and annotator expertise in hate speech detection.
• Pulastya et al. (2021) — Assessing the Quality of the Datasets by Identifying Mislabeled Samples — proposes AQUAVS, a supervised variational autoencoder with auxiliary discriminative network, to automatically identify mislabeled data points via outlier detection in latent space; demonstrates high-precision mislabel identification without requiring clean validation data or prior knowledge of noise type; shows significant accuracy improvements on downstream classification tasks after filtering identified mislabeled samples, especially on MNIST and CIFAR-10.
• Zannettou et al. (2018) — The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans — comprehensive typology of false information ecosystem identifying eight types of false information, twelve actor categories, and six motives; surveys 200+ papers on user perception, propagation dynamics, detection, containment, and political misinformation.
• Lazer et al. (2018) — The Science of Fake News — multidisciplinary Science article synthesizing knowledge on fake news prevalence, impact, psychological mechanisms, and interventions (individual and platform-based); identifies major gaps and calls for industry-academic collaboration; canonical framework for the field.
• Mohseni & Ragan (2018) — Combating Fake News with Interpretable News Feed Algorithms — position paper reviewing fake news detection methods and arguing that transparent, interpretable news feed algorithms could mitigate misinformation amplification by increasing user awareness of algorithmic curation; identifies echo chambers and filter bubbles as key mechanisms of harm.
• Guess, Nagler & Tucker (2019) — Less than you think: Prevalence and predictors of fake news dissemination on Facebook — empirical study linking survey data (N=3,500) to Facebook profiles (N=1,191); establishes that fake news sharing during 2016 was rare (8.5% of users); identifies age as the strongest demographic predictor—users 65+ shared nearly 7 times as many fake news articles as those 18–29; effect persists after controlling for ideology and education.
• Tandoc, Lim & Ling (2017) — Defining "Fake News": A typology of scholarly definitions — systematic review of 34 academic studies (2003–2017) that define and operationalize "fake news"; proposes a two-dimensional typology (facticity × intent to deceive) identifying six types: satire, parody, fabrication, manipulation, advertising, and propaganda; foundational for conceptual clarity in the field.
• Hassan et al. (2018) — ClaimBuster: The first end-to-end fact-checking system
• Hassan et al. (2017) — Claim Ranking in Political Debates
• Bikhchandani, Hirshleifer & Welch (1992) — A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades
• Lewandowsky et al. (2012) — Misinformation and its correction: Continued influence and successful debiasing — Psychological Science in the Public Interest foundational review synthesizing literature on why false beliefs persist despite corrections; examines cognitive mechanisms (mental models, source confusion, fluency, coherence) and proposes evidence-based debiasing strategies (warnings, alternative explanations, repeated corrections, worldview-consonant framing) with practical guidance for practitioners.
• Krishna et al. (2026) — Modeling Duelling Contagions of True and False Information in the Face of Inherent Individual biases — agent-based co-diffusion model integrating complex contagion, spiral of silence, and cognitive biases (optimism, confirmation, false consensus); validates predictions against two large-scale online behavioral experiments; demonstrates manipulative narratives dominate when early spreaders hold them and cognitive biases amplify both true and false information; counterfactual analysis shows strategic seeding of self-censoring agents or deployment of well-informed external agents can shift equilibrium toward accurate beliefs; extends cascade models by grounding agent behavior in empirically-validated psychological theories.
• Zhou, Zafarani, Shu & Liu (2019) — Fake News: Fundamental Theories, Detection Strategies and Challenges — WSDM '19 tutorial survey synthesizing 20+ interdisciplinary theories (psychology, social science, economics, forensics) explaining why misinformation succeeds and why people spread it; unified framework of four detection perspectives (knowledge, style, propagation, credibility); identifies open challenges in timeliness, cross-domain transfer, and efficiency for real-world deployment.
• Douglas, Sutton & Cichocka (2017) — The Psychology of Conspiracy Theories — Current Directions in Psychological Science review synthesizing two decades of empirical research; proposes unified taxonomy of epistemic (seeking understanding and certainty), existential (seeking control and security), and social (seeking positive in-group identity) motives driving conspiracy-theory belief; critical finding that conspiracy theories appear to frustrate rather than satisfy these underlying needs, making them a self-defeating form of motivated reasoning.
• van Prooijen & Douglas (2018) — Belief in conspiracy theories: Basic principles of an emerging research domain — European Journal of Social Psychology 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); provides organizing framework for understanding why conspiracy theories appeal across diverse contexts.
• Acerbi (2019) — Cognitive attraction and online misinformation — Palgrave Communications content analysis of 260 articles from 26 hoax websites showing that misinformation succeeds due to psychological appeal rather than media inefficiency; 86% of articles contain threat-related content (28%), negative framing (49%), social information (50%), or cognitive-preference elements; reframes misinformation as "high-quality" when measured by cognitive appeal, not truthfulness.
• Treen, Williams & O'Neill (2020) — Online misinformation about climate change — comprehensive literature review synthesizing research across communication, psychology, computer science, and political science on climate change misinformation; defines concepts (misinformation vs. disinformation), identifies actor networks (scientists, governments, industry, media, think tanks), examines spread mechanisms via social media (homophily, echo chambers, algorithmic bias), analyzes impacts on policy and public attitudes, reviews countermeasures (inoculation, correction, detection, platform mechanisms).
• Farrell (2016) — Corporate funding and ideological polarization about climate change — PNAS empirical study combining Structural Topic Modeling on 40,785 texts with organizational network analysis of 164 climate contrarian organizations (1993–2013); demonstrates that corporate funding from ExxonMobil and Koch foundations directly influences thematic content of polarization efforts, with funded organizations emphasizing energy-production-friendly and scientific-skepticism frames; provides empirical evidence for long-suspected dynamics of how private funding shapes public scientific discourse.
• van der Linden et al. (2017) — Inoculating the Public against Misinformation about Climate Change — large-scale randomized experiments (N=2,167) testing attitudinal inoculation against competing consensus-related misinformation; consensus messaging increases perceived agreement 20 percentage points, but is nullified by competing claims; pre-emptive warnings and refutations preserve two-thirds of the effect across political spectrum, with no evidence of backfire.
• Cook, Lewandowsky & Ecker (2017) — Neutralizing misinformation through inoculation: Exposing misleading argumentation techniques reduces their influence — PLOS ONE empirical study (N=714 and 392 in two experiments) testing inoculation theory on climate change misinformation; demonstrates that pre-exposure to explanations of flawed argumentation techniques (false balance, fake experts) neutralizes misinformation and reduces politically motivated polarization.
• Pennycook et al. (2021) — Shifting attention to accuracy can reduce misinformation online — Nature paper demonstrating that subtle reminders to focus on accuracy increase sharing of accurate news across six survey experiments and a Twitter field experiment; identifies limited attention (not confusion or indifference) as the primary mechanism driving misinformation sharing; shows that reorienting people's limited cognitive attention toward accuracy can substantially improve the quality of shared information online.
• Guess et al. (2020) — A digital media literacy intervention increases discernment between mainstream and false news in the United States and India — large-scale RCT testing Facebook's "Tips to Spot False News" platform-based intervention across three samples; finds 26.5% improvement in discernment in nationally representative US sample, 17.3% in India online, and no effect in rural face-to-face sample; effects persist ~3 weeks in US but decay over time; demonstrates that simple, scalable media literacy teaching can improve news evaluation ability but effectiveness varies by digital experience and context.
• Roozenbeek & van der Linden (2019) — Fake news game confers psychological resistance against online misinformation — Palgrave Communications empirical study (N=15,000) demonstrating that active inoculation through a gamified ~15-minute intervention teaching six deception techniques (impersonation, polarisation, emotional manipulation, conspiracy theories, discrediting, trolling) significantly reduces perceived reliability of fake news across education, age, and political ideology; largest effect among those most vulnerable to misinformation.
• Ecker et al. (2022) — The psychological drivers of misinformation belief and its resistance to correction — Nature Reviews Psychology comprehensive review of cognitive, social, and affective mechanisms in false belief formation; barriers to belief revision (continued influence effect); evidence-based interventions (prebunking and debunking); implications for journalists, policymakers, information consumers, and health communicators.
• Wardle & Derakhshan (2017) — Information Disorder: Toward an Interdisciplinary Framework for Research and Policy Making — canonical framework distinguishing mis-, dis-, and mal-information by falseness and intent-to-harm; agent-message-interpreter model; creation-production-distribution lifecycle analysis; 34 policy recommendations for platforms, governments, media, and civil society.
• Jack (2024) — Lexicon of Lies: Terms for Problematic Information — Data & Society practitioner's guide to terminology; clarifies distinctions between misinformation, disinformation, propaganda, information operations, gaslighting, and related concepts; examines challenges in establishing intent and cross-cultural complications.
• Marwick & Lewis (2017) — Media Manipulation and Disinformation Online — Data & Society ecosystem-level analysis of internet subcultures' tactics, actors, and platform vulnerabilities; case studies of Gamergate, Pizzagate, and 2016 election manipulation.
• Papasavva et al. (2021) — The Gospel According to Q: Understanding the QAnon Conspiracy from the Perspective of Canonical Information — empirical study of the QAnon conspiracy theory analyzing 4,961 unique Q drops from six aggregation sites, 121,956 Reddit posts, and related 4chan/8kun content; demonstrates poor canonicalization of Q drops across aggregation sites, provides stylometric evidence of multiple authors, and traces QAnon's transition from fringe imageboards to mainstream social networks via Reddit's crucial intermediary role.
• Allcott & Gentzkov (2017) — Social Media and Fake News in the 2016 Election — first comprehensive empirical evidence on fake news exposure; database of 156 election-related false stories, web traffic data, and post-election survey of 1,208 adults; estimates average American saw 1.14 fake articles; documents 3:1 partisan asymmetry (pro-Trump articles shared 30M times vs. pro-Clinton 7.6M); economic model of fake news supply/demand; argues electoral impact smaller than single TV ad.
• Sahly, Shao & Kwon (2019) — Social Media for Political Campaigns: An Examination of Trump's and Clinton's Frame Building and Its Effect on Audience Engagement — comparative content analysis of frame building in 2016 campaign across Twitter (3,805 Trump, 655 Clinton tweets) and Facebook (655 posts); finds Trump relied on conflict and negative emotion frames while Clinton used morality and positive frames; frame effects on engagement (retweets, shares) consistent on Twitter but platform-specific on Facebook
• Nelson & Taneja (2018) — The small, disloyal fake news audience — empirical audience measurement using comScore data showing fake news reaches only 675K unique monthly visitors vs. 28M for real news; applies the Law of Double Jeopardy to show fake news audiences are small and disloyal; demonstrates that audience availability (time spent online) is a stronger predictor of misinformation exposure than demographics; shows 80% of fake news traffic originates from social platforms, particularly Facebook.
• Allen et al. (2020) — Evaluating the fake news problem at the scale of the information ecosystem — multimode national dataset (Nielsen TV, Comscore desktop/mobile) spanning 2016–2018; 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 rather than overt fakery.
• Helmus et al. (2018) — How to Counter Russian Social Media Influence in Eastern Europe — RAND Corporation report analyzing Russian state-sponsored social media campaigns; documents coordinated troll networks, bot accounts, fake hashtags, and nonattributed comments targeting Eastern European publics; mixed-methods approach combining quantitative social media analysis with expert interviews; identifies counter-strategies including accelerated detection, alternative narratives, and institutional capacity building.
• Friggeri et al. (2014) — Rumor Cascades — large-scale empirical study of 16,672 rumor cascades on Facebook using Snopes.com ground truth; shows rumor cascades run deeper than typical content; finds true rumors more viral than false despite false rumors dominating uploads (62% vs. 45% on Snopes); Snopes fact-checks increase deletion likelihood 4.4× for false rumors but have minimal long-term propagation effects; demonstrates rumor mutation and variant selection over time.

Network and graph algorithms

• A Decision-Based Heterogeneous Graph Attention Network for Multi-Class Fake (2025) — DHGAT: Decision-based Heterogeneous Graph Attention Network enabling per-node per-layer dynamic selection of optimal neighborhood types for multi-class fake news detection. Models news as heterogeneous graphs with nine edge types derived from speaker metadata (speaker, context, subject, party, job-title, state, KNN-5/6/7); decision network selects best neighborhood type via Gumbel-Softmax; representation network updates embeddings. Evaluated on LIAR (12,836 items, six classes); achieves ~4% accuracy improvement over baselines in label-scarce regimes (10%–30% labeled); custom semantic distance loss exploits ordinal label structure (pants-fire < false < barely-true < half-true < mostly-true < true).
• Neighborhood-Order Learning Graph Attention Network for Fake News Detection (2025) — NOL-GAT: Addresses a fundamental limitation of standard GNNs (fixed L-hop neighborhoods) by allowing each node to adaptively learn its optimal neighborhood order at every layer via Gumbel-Softmax. Architecture comprises a Hop Network (order selection) and Embedding Network (representation updates). Tested on five fake news datasets (Fake.Br, Fact-checked News, FakeNewsNet, FakeNewsDetection, FakeNewsData) achieving significant improvements in low-label regimes (10%–30% labeled data); outperforms baselines (CO-GNN, L2Q, TGNcl, TextGCN, LSTM-CP-GCN, GATv2) by 5–15% macro-F1.
• Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for (2025) — InSIDE: Graph-based intent-semantic joint learning for fake news detection. Models news as two heterogeneous graphs capturing semantic signals (sentence-entity network with local narrative flow and global entity-mediated context) and intent signals (coarse-to-fine hierarchy of purposes/motivations). Dynamic pathway-based alignment via pseudo nodes bridges semantic and intent representations. Achieves consistent improvements across four datasets (up to 9.12% F1 on GossipCop) outperforming intent-agnostic and intent-aware baselines; demonstrates framework adaptability across intent conceptualizations.
• Mao et al. (2024) — Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques — comprehensive survey of integrating LLMs with graph representation learning; proposes novel taxonomy decomposing models into primary components (knowledge extractors for attributes, structures, and labels; knowledge organizers as GNN-centric, LLM-centric, or hybrid) and operation techniques (integration strategies at input, hidden, and alignment-based levels; training strategies via pre-training, prompting, and instruction tuning); essential framework for understanding emerging graph foundation models that combine graph structure with semantic information.
• Vatter, Mayer & Jacobsen (2023) — The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey — comprehensive survey of distributed systems for scalable GNN training, bridging graph processing systems (Pregel, PowerGraph, GraphLab) and DNN training frameworks; systematically categorizes partitioning strategies, sampling techniques, inter-process communication, synchronization modes, and programming abstractions across 20+ systems (DGL, GraphSAINT, DistDGL, etc.); essential for understanding how to scale GNN-based misinformation detection to large social networks.
• Dai et al. (2023) — A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability in ACM Computing Surveys — comprehensive survey of trustworthy GNN research covering privacy attacks (membership inference, property inference, reconstruction) and defenses (differential privacy, federated learning, machine unlearning), adversarial robustness methods, fairness approaches to prevent discrimination, and explainability techniques; essential for understanding GNN-based approaches to misinformation detection and their real-world deployment challenges.
• Fortunato, S. (2009) — Community detection in graphs — comprehensive 103-page survey covering algorithm taxonomy (hierarchical clustering, spectral methods, modularity optimization), theoretical foundations (NP-hardness, quality functions), benchmarking on standard networks, and applications across biological, social, and technological networks; provides foundational theory and methods for network-based approaches to misinformation detection and information diffusion analysis.

• Cai & Zafarani (2026) — Spectral Analysis of Fake News Propagation — develops spectral view of propagation structure by deriving 35 spectral bounds organized into five categories capturing branching capacity, cascade scale, structural cohesion, propagation span, and diffusion dynamics; shows spectral representations distinguish fake from real news (59.4% accuracy on Weibo22, 33.55% on Twitter16) while providing interpretability through explicit structural-property correspondence; demonstrates fake news exhibits deeper, more unbalanced cascade structures through discrete structural optimization algorithms.

• Community detection in graphs — 2009 · arXiv

• The structure and function of complex networks — 2003 · Reviews of Modern Physics
• The structure and function of complex networks — 2003 · Reviews of Modern Physics

Rumour verification and stance

• Zubiaga et al. (2016) — Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations — introduces Linear and Tree Conditional Random Fields (CRF) to exploit sequential and tree-structured patterns in rumour conversations; demonstrates that modelling conversational tree structure significantly improves stance classification compared to non-sequential baselines on PHEME dataset
• Kochkina, Liakata & Augenstein (2017) — Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM — best-performing system in RumourEval 2017 Subtask A; proposes Branch-LSTM architecture that decomposes conversation trees into linear branches and models them sequentially; achieves 78.4% accuracy using LSTM layers processing tweet sequences with word2vec and hand-crafted lexical/relational features.
• Kumar & Carley (2019) — Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations — proposes Tree LSTM and Binarized Constituency Tree (BCTree) LSTM with convolution units for joint stance and veracity classification; uses multi-task learning showing complementary task benefits; achieves 0.520 mean F1 for stance and 0.379 for veracity on PHEME dataset, outperforming prior work by 12% and 15% respectively.
• Zubiaga et al. (2018) — Detection and Resolution of Rumours in Social Media: A Survey — authoritative survey of end-to-end rumour classification pipeline: definition and typology (newly emerging vs. long-standing), data collection APIs and annotation schemes, detection approaches for both rumour types, tracking and filtering, stance classification (SDQC: Support/Deny/Query/Comment), and veracity classification; reviews datasets (PHEME, RumourEval), approaches from feature engineering to deep learning, and key challenges including class imbalance, vocabulary drift, and cross-platform generalization.
• Kochkina, Liakata & Zubiaga (2018) — All-in-one: Multi-task Learning for Rumour Verification — demonstrates multi-task learning improvements for veracity classification by jointly training with stance and detection as auxiliary tasks; shows 13.6% improvement over single-task baselines on RumourEval and up to 28.9% on PHEME; analyzes link between dataset properties (kurtosis, entropy) and MTL effectiveness.
• Hardalov et al. (2021) — A Survey on Stance Detection for Mis- and Disinformation Identification — comprehensive survey of stance detection (determining whether a text supports, denies, questions, or comments on a claim) reviewing task formulations, datasets across 15+ benchmarks and multiple languages, and approaches from feature engineering to pre-trained language models; covers applications to fact-checking, rumour verification, and propaganda detection.

Stance detection

Shared tasks and benchmarks

• The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength — Clickbait Challenge 2017 shared task with 38,517 graded-scale annotated tweets; 13 submitted systems achieving significant performance gains over prior baselines; reformulates clickbait detection as regression to measure strength rather than binary classification; introduces Webis Clickbait Corpus 2017
• A Benchmark Study of Machine Learning Models for Online Fake News Detection — Comprehensive empirical benchmark comparing 19 machine learning models (8 traditional, 6 deep learning, 5 pre-trained transformers) on three fake news datasets spanning politics, health, and diverse topics; finding: BERT-based pre-trained models (RoBERTa 96% accuracy on large datasets, >90% with 500 samples) substantially outperform traditional ML and deep learning approaches; practical guidance for practitioners across resource constraints.
• RumourEval 2019: Determining Rumour Veracity and Support for Rumours — extended shared task with Twitter and Reddit data; two subtasks: (A) SDQC stance classification on 8,574 conversation posts, (B) veracity prediction on 446 rumours; 22 system submissions (70% increase from 2017); best systems employ pre-trained contextual embeddings (BERT, GPT); demonstrates that conversation structure and ensemble approaches advance rumour verification beyond single-task specialization.
• SemEval-2017 Task 8: RumourEval — benchmark shared task establishing foundation for rumour verification research; defines two subtasks: (a) SDQC stance classification (Support/Deny/Query/Comment) of replies to rumourous claims, and (b) veracity prediction (true/false) of source tweets; provides datasets from 10 events with 297 training threads, 28 test threads, and 1,080 tweets; 13 systems from 4 continents participated; results show stance classification achievable (78% best), but veracity prediction remains hard (below baselines).
• Kochkina, Liakata & Augenstein (2017) — Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM — best-performing system in RumourEval 2017 Subtask A; proposes Branch-LSTM architecture that decomposes conversation trees into linear branches and models them sequentially; achieves 78.4% accuracy using LSTM layers processing tweet sequences with word2vec and hand-crafted lexical/relational features.
• Thorne et al. (2018) — The Fact Extraction and VERification (FEVER) Shared Task — first shared task combining evidence retrieval and natural language inference for fact verification; 23 teams, 185,445 human-generated claims verified against Wikipedia; best system achieves 64.21% FEVER score; analysis reveals three-stage pipeline architecture (document selection → sentence selection → NLI) is dominant; post-competition evidence augmentation identified 308 new evidence sets and corrected label errors.
• Da San Martino, Barrón-Cedeño & Nakov (2019) — Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection — shared task on propaganda technique identification in news articles; two subtasks: FLC (fragment-level with 18-way technique classification) and SLC (sentence-level binary); 90 registered teams with 39 submitting predictions; winning systems use fine-tuned BERT achieving 0.63 F1 (SLC) and 0.25 F1 (FLC); corpus of 497 annotated articles with fragment-level annotations enables interpretable propaganda analysis.

Datasets and resources

• Zampieri et al. (2019) — Predicting the Type and Target of Offensive Posts in Social Media — OLID: 14,100 English tweets annotated with hierarchical three-level schema (offensive detection, type categorization: targeted/untargeted, target identification: individual/group/other); foundational dataset for offensive language detection, hate speech, and cyberbullying; baseline CNN achieves 0.80 macro-F1 on offensive detection.
• Hanselowski et al. (2019) — A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking — SNOPES corpus with 6,422 validated claims and 14,296 documents from heterogeneous web sources; comprehensive annotations for document retrieval, evidence extraction (fine-grained), stance detection, and claim validation; demonstrates that multi-domain, multi-source fact-checking is more challenging than Wikipedia-only datasets
• Horne et al. (2018) — Sampling the News Producers: A Large News and Feature Data Set for the Study of the Complex Media Landscape (NELA2017) — 1,586 articles from 92 diverse news sources (mainstream, hyper-partisan, satire, misinformation); 130 content-based linguistic and engagement features enabling source characterization; foundation for the NELA-GT series.
• Dolhansky et al. (2020) — The DeepFake Detection Challenge (DFDC) Dataset — largest deepfake detection benchmark with 128,154 videos from 3,426 consenting actors and >100,000 face-swapped clips; addresses ethical limitations of prior datasets; includes diverse face-swap generation methods (DFAE, MM/NN, NTH, FSGAN, StyleGAN); public Kaggle competition demonstrates detection remains unsolved despite scale and diversity.
• Gruppi, Horne & Adalı (2022) — NELA-GT-2022: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles — 1.78M news articles from 361 outlets spanning all of 2022 with source-level veracity labels from Media Bias/Fact Check (factuality scores 0–5, conspiracy/pseudoscience classification) and 346K embedded tweets; fifth release in NELA-GT series providing 5.5+ years of longitudinal coverage; benchmark for robust fake news detection and event-driven media analysis.
• r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News: Introduces Fakeddit, a large-scale multimodal dataset with 1.06M Reddit submissions, 64% text+image pairs, and 2-way/3-way/6-way labels; demonstrates that multimodal models (BERT + ResNet50 with maximum fusion) achieve 85.88% 6-way accuracy, ~10 percentage points above text-only baselines; identifies satire and imposter content as hardest categories.
• Wang (2017) — Liar, Liar Pants on Fire: A New Benchmark Dataset for Fake News Detection — 12,836 labeled political statements from PolitiFact spanning 2007–2016 with 6-way fine-grained labels (pants-fire, false, barely-true, half-true, mostly-true, true) and rich metadata (speaker identity, party, state, job, credit history); detailed fact-check justifications and supporting documents; foundational benchmark for statement-level fact-checking research.
• Augenstein et al. (2019) — MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims — largest publicly available real-world fact-checking dataset with 34,918 naturally occurring claims from 26 fact-checking websites; includes entity linking to Wikipedia (25,763 unique entities) and evidence pages retrieved via Google Search; multi-task learning approach for veracity prediction across domains with heterogeneous label schemas achieves 49.2% Macro F1
• Thorne et al. (2018) — FEVER: A Large-Scale Dataset for Fact Extraction and VERification — dataset of 185,445 human-verified claims with Wikipedia evidence; three-class labels (SUPPORTED/REFUTED/NOT ENOUGH INFO) with sentence-level evidence annotation; baseline system combines document retrieval, sentence selection, and textual entailment achieving 31.87% accuracy with correct evidence requirement

• Shu et al. (2018) — FakeNewsNet: A Data Repository with News Content, Social Context and Spatiotemporal Information for Studying Fake News on Social Media — multi-dimensional benchmark dataset containing PolitiFact (12,911 articles) and GossipCop (22,140 articles) with news content (text, images), social context (user engagement, network structure, user profiles), and spatiotemporal information (location, timestamps); integrates multiple feature families enabling research into detection, evolution, mitigation, and bot detection.

• NELA-GT-2018: A Large Multi-Labelled News Dataset for The Study of Misinformation — 2019 · arXiv

Media profiling and source credibility

• Baly et al. (2018) — Predicting Factuality of Reporting and Bias of News Media Sources — predicts news outlet-level factuality (low/mixed/high) and political bias (7-point scale) using 141 features from article text, Wikipedia, Twitter, URL structure, and web traffic; introduces 1,066-website dataset manually annotated for both tasks; demonstrates textual features most predictive for factuality (58.02 Macro-F₁), while Wikipedia and Twitter features important for bias; feature ablation shows complementary value of diverse signals
• Baly et al. (2019) — Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media — extends media profiling to jointly model trustworthiness (3-point scale) and political ideology (7-point scale) via Copula Ordinal Regression; multi-task learning with auxiliary tasks reduces mean absolute error; shows correlation between extreme bias and low factuality
• Baly et al. (2020) — What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context — combines article text features with YouTube audience demographics and Wikipedia content for outlet-level bias and factuality prediction; shows multimodal approach improves performance over text-only baselines
• Nakov et al. (2021) — A Survey on Predicting the Factuality and the Bias of News Media — comprehensive survey of media-level profiling methods; reviews textual, multimedia, audience, and infrastructure features; discusses challenges in ordinal bias scales, multimodality, and temporal variation

Deception and behavioral detection

• A Deep Learning Approach for Multimodal Deception Detection — Multimodal neural networks for deception detection using 3D-CNN on video, audio features, textual CNN, and micro-expressions; achieves 96.14% accuracy on courtroom trial videos, substantially outperforming traditional classifiers

Mainstream media dissemination

• Mihailidis & Viotty (2017) — Spreadable Spectacle in Digital Culture: Civic Expression, Fake News, and the Role of Media Literacies in "Post-Fact" Society — critical analysis of how mainstream media coverage paradoxically legitimizes spectacle while attempting to debunk it; uses Pizzagate conspiracy theory as case study showing how initial Reddit-based spectacle spreads through online communities, then becomes amplified by mainstream reporting; argues media literacy must be repositioned toward critique, creation, and civic engagement rather than individual fact-checking.
• Tsfati et al. (2020) — Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis — comprehensive literature review examining the understudied role of mainstream outlets (not fake news websites) in disseminating misinformation; identifies four structural reasons why mainstream media cover fake news (journalistic duty to expose falsehood, news values criteria, psychology of newsroom decision-making, technical infrastructure for monitoring); synthesizes psychological mechanisms explaining why mainstream coverage of false claims often backfires (fluency effects, negation accessibility, mental model persistence, motivated reasoning); argues mainstream media paradoxically function as both important correctors and amplifiers of misinformation.

COVID-19 pandemic infodemic

• Memon & Carley (2020) — Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset — Twitter dataset (CMU-MisCOV19) with 4,573 manually annotated tweets across 17 categories; compares misinformed vs. informed communities on network structure (density), bot coordination (19% vs. 11%), linguistic patterns (narratives), and vaccination stance; finds misinformed communities denser and more organized, with evidence of organized disinformation campaigns.
• van der Linden, Roozenbeek & Compton (2020) — Inoculating Against Fake News About COVID-19 — perspective on applying psychological inoculation to the COVID-19 infodemic; documents scope of pandemic misinformation (46–48% exposure in UK/US; 25%+ of top YouTube videos misleading); links conspiracy belief to vaccine hesitancy, reduced compliance, and violent intentions; proposes prebunking via gamified interventions (Bad News, Go Viral!) as scalable alternative to reactive fact-checking.
• Cui & Lee (2020) — CoAID: COVID-19 Healthcare Misinformation Dataset — benchmark dataset with 4,251 news articles (204 fake, 3,565 true), 482 claims, 926 social posts, and 296,000 user engagements (tweets/replies); multiplatform (websites, Twitter, Facebook, Instagram, YouTube, TikTok); benchmarks detection methods from SVM to state-of-the-art attention-based models.
• Fighting an Infodemic: COVID-19 Fake News Dataset — dataset of 10,700 COVID-19 posts/articles with binary real/fake labels collected from social media and fact-checking websites; benchmarks ML baselines achieving 93.32% F1-score with SVM.
• Cinelli et al. (2020) — The COVID-19 Social Media Infodemic — large-scale comparative analysis of COVID-19 information diffusion across five platforms (Twitter, Instagram, YouTube, Reddit, Gab) with 1.3M+ posts from 3.7M+ users; applies epidemic models (EXP and SIR) to estimate platform-specific basic reproduction numbers (R₀); all platforms show infodemic conditions (R₀ > 1); characterizes platform-specific amplification of misinformation with coefficients ranging from 10% (YouTube) to 400% (Gab).
• Pennycook et al. (2020) — Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention — pre-registered experiments (1,700+ participants) demonstrating accuracy-salience nudge intervention triples discernment in shared content decisions; effects uniform across partisanship, education, and geographic proximity to COVID-19.
• Lee et al. (2020) — Misinformation Has High Perplexity — proposes language model perplexity as a falseness signal for evidence-based debunking; achieves 75% accuracy on scientific COVID-19 claims using GPT-2 with minimal labeled data; releases Covid19-scientific and Covid19-politifact test sets.
• Roozenbeek et al. (2020) — Susceptibility to misinformation about COVID-19 around the world — international survey of 3,750 adults across five countries identifying psychological predictors of COVID-19 misinformation belief; susceptibility predicts vaccine hesitancy and reduced health-guidance compliance.
• Zhou et al. (2020) — ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research — multimodal dataset of 2,029 COVID-19 news articles with credibility labels; benchmarks SAFE achieving best F₁ 0.833/0.672.
• Yang et al. (2020) — CHECKED: Chinese COVID-19 Fake News Dataset — first Chinese-language COVID-19 misinformation dataset with 2,104 Weibo microblogs, expert labels, and 1.87M repost propagation graphs.
• Li et al. (2020) — MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation — multilingual multimodal dataset spanning English, Arabic, Chinese with verified credibility labels.

• Du et al. (2021) — Cross-lingual COVID-19 Fake News Detection — addresses misinformation in low-resource languages (Chinese) by training on English COVID-19 news and applying via machine translation; CrossFake framework using BERT with sub-text slicing achieves 75% accuracy on manually-curated Chinese dataset (86 fake, 114 real), outperforming monolingual and cross-lingual baselines.

• Prevalence of Health Misinformation on Social Media: Systematic Review — 2021 · Journal of Medical Internet Research

• A large-scale COVID-19 Twitter chatter dataset for open scientific research — 2020 · arXiv

Synthetic media and deepfakes

• Mirsky & Lee (2020) — The Creation and Detection of Deepfakes: A Survey — comprehensive 38-page survey covering both creation and detection methodologies; systematically reviews generative architectures (GANs, VAEs, CNNs, RNNs), technical approaches to reenactment/replacement/editing/synthesis, artifact-specific and undirected detection methods; identifies arms race dynamics and current technological limitations.
• Tolosana et al. (2020) — DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection — comprehensive survey of facial manipulation techniques (entire face synthesis, identity swap, attribute manipulation, expression swap) and detection methods; covers GAN-based generation (StyleGAN, ProGAN), public databases, and state-of-the-art benchmarks showing detection difficulty under cross-domain conditions.
• Rana et al. (2022) — Deepfake Detection: A Systematic Literature Review — comprehensive SLR of 112 deepfake detection papers (2018–2020) with rigorous methodology; organizes 77% deep learning (primarily CNNs), 18% machine learning, 3% statistical, and 2% blockchain-based techniques; synthesizes datasets (FaceForensics++, DFDC, DeeperForensics); evaluates 100+ detection models and 10+ feature types; finds deep learning achieves 89.7% mean accuracy vs. 85% for traditional ML; identifies standardization gaps in evaluation and future research directions.
• Ba et al. (2024) — Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection — information-theoretic framework decomposing facial features into disentangled local representations and aggregated global representations using mutual information losses; achieves 0.983 AUC on FaceForensics++, 0.999 AUC on Celeb-DF-V2, 0.939 AUC on DFDC; demonstrates strong cross-dataset generalization (0.818-0.864 AUC on Celeb-DF when trained on FaceForensics++); addresses overfitting limitations of prior region-specific detection methods.
• Cifci, Demir & Yin (2019) — FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals — detects deepfakes via photoplethysmography (blood flow patterns) by analyzing spatial coherence and temporal consistency of biological signals; achieves 91%+ accuracy on Face Forensics++, CelebDF, and UADFV; introduces "in the wild" Deep Fakes Dataset; demonstrates biological signal inconsistencies as orthogonal detection signal to visual artifacts.
• Sabir et al. (2019) — Recurrent Convolutional Strategies for Face Manipulation Detection in Videos — recurrent-convolutional networks exploiting temporal discrepancies for detecting Deepfake, Face2Face, and FaceSwap; combines face alignment preprocessing with bidirectional GRU cells operating on frame sequences; achieves 96.9%, 94.35%, and 96.3% accuracy respectively on FaceForensics++, improving prior state-of-the-art by up to 4.55%; shows bidirectional temporal recurrence essential while multi-level recurrence hurts due to limited training data.
• DeepFakes: a New Threat to Face Recognition? Assessment and Detection — first publicly available GAN-based Deepfake database (620 videos from 16 VidTIMIT subject pairs); demonstrates that VGG and FaceNet face recognition systems achieve FAR of 85.62% and 95.00% on high-quality deepfakes; evaluates detection methods showing audio-visual lip-sync approaches fail entirely while image quality metrics achieve 8.97% EER
• Zhou et al. (2018) — Two-Stream Neural Networks for Tampered Face Detection — two-stream architecture combining spatial and frequency domain analysis for detecting face tampering and deepfakes.
• Gehrmann et al. (2019) — GLTR: Statistical Detection and Visualization of Generated Text — statistical approach for detecting automatically generated text using language model probabilities and visualization techniques.
• Rössler et al. (2019) — FaceForensics++: Learning to Detect Manipulated Facial Images — largest facial forgery benchmark (1.8M+ images from 1K+ videos) with four manipulation methods (Face2Face, FaceSwap, DeepFakes, NeuralTextures); comprehensive evaluation of detection methods from stegananalysis to CNN-based approaches; human baseline (68.7% accuracy) vs. XceptionNet (99.26%); systematic analysis of compression robustness showing significant performance degradation under realistic post-processing.
• Li, Chang & Lyu (2018) — In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking — detects deepfakes via physiological signal absence (eye blinking); LRCN model combining CNN feature extraction with LSTM temporal modeling achieves 0.99 AUC vs. 0.98 for CNN-only and 0.79 for hand-crafted baselines; exploits fact that deepfake training datasets rarely contain closed-eye images, making natural blink sequences absent from synthesized video.
• McCloskey & Albright (2018) — Detecting GAN-generated Imagery using Color Cues — forensic detection of GAN-generated images by analyzing generator network architecture; identifies two cues (color channel overlap and saturation suppression) that distinguish GANs from real cameras; saturation-based SVM achieves 0.7 AUC on fully GAN-generated images and 0.61 on face-swapped images.
• Vaccari & Chadwick (2020) — Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in News — experimental study (N=2,005 UK respondents) on political deepfakes using the widely-circulated Obama/Peele deepfake; finds that deepfakes increase uncertainty about content, and this uncertainty mediates reduced trust in news on social media; deepfakes threaten civic culture through epistemic erosion rather than mass deception; educational interventions showing deepfakes are synthetic can mitigate effects.
• Fagni et al. (2020) — TweepFake: about detecting deepfake tweets — first public dataset of human vs. machine-generated tweets; 25,572 tweets from 23 bot accounts (GPT-2, RNN, LSTM, Markov, etc.) and 17 human accounts; benchmarks 13 detection methods; finds transformer-based fine-tuned models (RoBERTa) achieve 90% accuracy, character-level encodings effective for short text, but GPT-2 tweets remain challenging (65–80% accuracy).
• Yang, Li & Lyu (2018) — Exposing Deep Fakes Using Inconsistent Head Poses — forensic detection method exploiting facial landmark misalignment in deepfake generation pipeline; compares 3D head poses estimated from all facial landmarks vs. central region only; real faces show consistent poses (cosine distance <0.02) while deepfakes exhibit large divergence (0.02–0.08); SVM classifier achieves 89.0% AUROC on deepfake videos and 84.3% on diverse face-swap dataset; demonstrates that synthesis errors invisible to human eye are detectable through geometric constraints.
• Afchar et al. (2018) — MesoNet: A Compact Facial Video Forgery Detection Network — lightweight CNN architectures (Meso-4 and MesoInception-4) for detecting Deepfake and Face2Face forgeries at mesoscopic level; achieves 98% detection accuracy for Deepfake and 95% for Face2Face under realistic compression; introduces first publicly available Deepfake dataset with 175 videos; demonstrates that efficient networks with ~28K parameters match or exceed complex architectures while remaining computationally practical.

Offensive AI & threat modeling

• Mirsky et al. (2021) — The Threat of Offensive AI to Organizations — comprehensive survey of 33 offensive AI capabilities (OACs) adversaries use to attack organizations, categorized into automation, campaign resilience, credential theft, exploit development, information gathering, social engineering, and stealth. Through expert user study (N=22), ranks threats by profit/achievability/defeatability/harm, finding exploit development, social engineering, and information gathering pose the greatest risk. Develops threat model T = H × (M/D) enabling practitioners to prioritize defensive investments. Particularly relevant for understanding AI-enabled social engineering attacks (deepfakes, impersonation, phishing) and model extraction threats to misinformation detection systems.

• SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social — 2019 · Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval)

• Comparative Studies of Detecting Abusive Language on Twitter — 2018 · arXiv
• Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media — 2018 · ICWSM (International Conference on Weblogs and Social Media)
• A Survey on Hate Speech Detection using Natural Language Processing — 2017 · Proceedings of the Fifth International Workshop on Natural Language Processing

AI safety & governance

• Mehrabi et al. (2023) — FLIRT: Feedback Loop In-context Red Teaming — Automated red teaming framework using in-context learning to generate adversarial prompts targeting generative models. Red language model generates prompts without fine-tuning; outputs are evaluated for safety, and feedback refines future generations. Proposes multiple attack strategies (FIFO, LIFO, Scoring, Scoring-LIFO) balancing effectiveness vs. diversity; demonstrates 80%+ attack success on vanilla Stable Diffusion and 60%+ on safeguarded variants, substantially outperforming prior manual and weakly-automated approaches. Shows attacks transfer across text-to-image models and extends to text-to-text models (GPT-Neo).
• Perez et al. (2022) — Discovering Language Model Behaviors with Model-Written Evaluations — proposes using language models to generate high-quality evaluations for testing diverse model behaviors. Generates 154+ datasets testing 154 behaviors across personality, goal-seeking, politics, and ethics. Discovers inverse scaling phenomena where larger models exhibit worse behavior on some safety-relevant tasks (stronger political views, greater desire to avoid shutdown, increased sycophancy). Shows RLHF training amplifies political bias and can create unintended instrumental subgoals. Demonstrates that smaller preference models effectively predict RLHF model behavior, enabling early detection of safety concerns before full deployment.
• Perez et al. (2022) — Red Teaming Language Models with Language Models — demonstrates automated red teaming to systematically discover harmful behaviors in language models. Uses one LM to generate adversarial test cases probing another LM for offensive replies, data leakage, personal information generation, and distributional biases. Explores zero-shot, few-shot, supervised, and reinforcement learning methods; RL achieves 27-42% offensive reply elicitation rates. Uncovers 1709 training data leakage instances and reveals that models discuss different demographic groups with significantly different offensiveness rates. Foundational work showing language models can complement human red teaming at scale.
• Bang et al. (2024) — Measuring Political Bias in Large Language Models: What Is Said and How It Is Said — evaluates political bias in LLM-generated content via two-tiered framework separating political stance (extreme anchor comparison) from framing bias (content and style components). Tests 11 open-source LLMs (LLaMa-2, Yi, Vicuna, Falcon, etc.) on 14 politically divisive topics; finds models exhibit liberal bias on social issues (same-sex marriage, climate change, public education), US-centric focus despite global training claims, and nuanced issue-specific biases varying across models. Decomposition of bias into content bias (entity/topic selection) and style bias (lexical polarity) provides explainable measurement beyond left-right spectrum.
• Goldstein et al. (2023) — Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations — threat assessment of how generative language models could expand the scale and sophistication of influence operations and propaganda campaigns. Uses ABC framework (Actors, Behaviors, Content) to analyze threats; proposes comprehensive mitigation taxonomy across four intervention points (model design & construction, model access, content dissemination, belief formation). Evaluates mitigations for technical feasibility, social feasibility, downside risks, and impact. Identifies critical unknowns about adoption rates, effectiveness, and norm-setting.
• [[2023-shu-exploitability-instruction-tuning|Shu et al. (2023) — On the Exploitability of Instruction Tuning]] — investigates vulnerabilities in instruction-tuned LLMs to data poisoning attacks via AutoPoison, an automated pipeline generating high-quality poisoned training data; demonstrates content injection and over-refusal attacks that scale to larger models while maintaining fluency; shows instruction tuning's low sample complexity is a double-edged sword enabling both capability learning and behavior hijacking.
• Evans et al. (2021) — Truthful AI: Developing and Governing AI That Does Not Lie — policy and governance framework for preventing AI systems from generating false or misleading statements. Proposes conceptual distinctions between lies, negligent falsehoods, and truthfulness; describes institutional arrangements for AI truthfulness standards (industry self-regulation, co-regulation, top-down regulation); outlines technical approaches to developing truthful AI. Argues early standards-setting is crucial before AI capabilities in strategic deception exceed human capacity.
• Wei, Haghtalab & Steinhardt (2023) — Jailbroken: How Does LLM Safety Training Fail? — analyzes fundamental failure modes in safety-trained language models through two mechanisms: competing objectives (where safety training conflicts with pretraining-induced instruction following) and mismatched generalization (where safety training fails to cover capabilities developed during pretraining). Develops 30 jailbreak methods and tests on GPT-4, Claude v1.3, and GPT-3.5 Turbo, finding vulnerabilities persist despite extensive red-teaming. Argues that scaling and additional red-teaming alone cannot resolve fundamental tensions in how LLMs are trained for safety.

Platform governance and regulation

• Gorwa (2019) — The platform governance triangle: conceptualising the informal regulation of online content — applies Abbott and Snidal's governance triangle framework to analyze how informal multi-stakeholder arrangements regulate online platform content across Europe. Maps regulatory initiatives by actor composition (state, firm, NGO) including NetzDG, AVMSD, Code of Conduct on Terror and Hate Content, Code of Practice on Disinformation, Facebook Oversight Board, and Global Network Initiative. Identifies three key dynamics shaping effectiveness: legitimation politics (contested authority), actor competencies (divergent expertise and capacity), and power relations (asymmetries favoring firms); argues effective governance requires collaboration across all three actor types, though such arrangements face ongoing tensions over legitimacy and influence.

• A Comprehensive Survey on Trustworthy Recommender Systems — 2022 · ACM Computing Surveys (to appear)

• A Survey on Computational Propaganda Detection — 2020 · arXiv

Propagation-based detection

• Shu, Bernard & Liu (2018) — Studying Fake News via Network Analysis: Detection and Mitigation — comprehensive chapter surveying network properties (echo chambers, filter bubbles, malicious accounts), three homogeneous and three heterogeneous network types, feature learning via network embeddings (NMF, RNNs), detection methods (interaction embedding, temporal diffusion, credibility propagation, knowledge network matching), and mitigation strategies (provenance identification, leader selection, influence minimization, mitigating campaigns).
• Cheng et al. (2014) — Can Cascades be Predicted? — foundational work showing cascades on social networks are highly predictable (~80% accuracy); temporal features most predictive, followed by structural, resharer, and user features; demonstrates prediction improves with observation window size; reveals fundamental differences between user-initiated and page-initiated cascades.
• Li et al. (2016) — DeepCas: an End-to-end Predictor of Information Cascades — first end-to-end deep learning approach to cascade size prediction; represents cascade graphs as random walk paths processed through bidirectional GRU with attention mechanisms; automatically learns cascade representations without hand-crafted features; evaluated on Twitter and academic citation cascades.
• Wang et al. (2017) — Topological Recurrent Neural Network for Diffusion Prediction — novel LSTM architecture for dynamic DAGs; models cascades as diffusion topologies showing information spread over network structure; learns topology-aware sender embeddings capturing both node properties and cascade dynamics; achieves 20–56% relative improvement over DeepCas across three real-world networks.
• Tacchini et al. (2017) — Some Like it Hoax: Automated Fake News Detection in Social Networks — hoax detection via user interaction patterns on Facebook; proposes that user "likes" encode veracity signals independent of content; two algorithms (logistic regression, harmonic boolean crowdsourcing) achieve >99% and 99.4% accuracy respectively; demonstrates transfer learning across Facebook communities and robustness with minimal labeled training data (<1% of posts); suggests diffusion patterns are a primary detection signal.
• Ruchansky, Seo, & Liu (2017) — CSI: A Hybrid Deep Model for Fake News Detection — three-module neural network combining text, temporal engagement response patterns, and user group behavior to identify fake news and suspicious users; Capture module uses LSTM on temporal features; Score module learns user suspiciousness from co-engagement patterns; achieves 89.2% accuracy on Twitter, outperforming text-only and propagation-only baselines; demonstrates value of joint modeling with fewer parameters than competing RNN approaches.
• Castillo, Mendoza & Poblete (2011) — Information Credibility on Twitter — foundational work framing Twitter credibility assessment via user reputation and propagation signals; 2,500+ trending topics, human-labeled for newsworthiness and credibility; shows user features (registration age, followers, activity) and propagation structure (retweet tree depth/breadth) are stronger predictors than text alone; achieves 89% accuracy on newsworthy detection, 86% on credibility classification.
• Ma, Gao & Wong (2017) — Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning — kernel-based approach (PTK/cPTK) measuring structural similarity between propagation trees; soft-matches subtrees to capture high-order patterns; context-sensitive extension considers propagation paths from root; extends to four-class classification (false/true/unverified/non-rumor); achieves 75.0% accuracy on Twitter15 with superior early detection (75% within 24 hours).
• Ma, Gao & Wong (2018) — Rumor Detection on Twitter with Tree-structured Recursive Neural Networks — applies recursive neural networks (bottom-up and top-down variants) to thread propagation trees; learns joint content-structure representations where local patterns (supportive/questioning replies) signal veracity; achieves 72.3% / 73.7% accuracy on Twitter15/16 with superior early detection (8 hours vs. 36 hours to match baseline).
• Vosoughi, Roy & Aral (2017) — The Spread of True and False News Online — largest longitudinal study of misinformation diffusion; ~126K verified true/false cascades from Twitter (2006–2017), 3M users, 4.5M shares; falsehood diffuses 6× faster to 1.5K people, reaches depth 19 vs. truth's depth 10 in 1/10 the time; 70% higher retweet likelihood; humans (not bots) responsible; novelty perception key driver.
• Shu et al. (2019) — Hierarchical Propagation Networks for Fake News Detection — constructs hierarchical propagation networks from both macro-level (retweet cascades) and micro-level (reply conversations) granularity; extracts structural, temporal, and linguistic features showing fake news spreads deeper, contains more bots, has shorter lifespan, and generates more negative sentiment; hierarchical network features (HPNF) achieve F1 > 0.80, outperforming prior macro-level-only approaches.
• Zhou & Zafarani (2019) — Network-based Fake News Detection: A Pattern-driven Approach — four network-structural patterns (More-Spreader, Farther-Distance, Stronger-Engagement, Denser-Network) across five network levels; 138 interpretable features; RF 0.929/0.932 accuracy/F₁ on PolitiFact without reading content; robust to limited early-stage network information.
• Monti et al. (2019) — Fake News Detection on Social Media using Geometric Deep Learning — graph convolutional networks on propagation cascades integrating user profiles, activity, social network structure, and spreading patterns; 92.7% ROC AUC on Twitter; user profile and network features are most important (90% AUC combined) while content marginally contributes; enables early detection within 1–2 hours with minimal cascade size (6 tweets).
• Dou et al. (2021) — User Preference-aware Fake News Detection (UPFD): Combines endogenous user preferences (from historical tweets) with exogenous news propagation patterns via GNNs. Encodes user preferences and news content using BERT/word2vec, builds Twitter retweet cascade graphs, uses GNN message passing to integrate signals; concatenates user engagement and news textual embeddings for classification. Achieves 84.62% accuracy on Politifact and 97.23% on Gossipcop; ablation studies show both user preference and propagation are necessary; demonstrates confirmation bias is a learnable signal in engagement data.

• Lu & Li (2020) — GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media — detects fake news on Twitter using only source tweet text and retweet user sequences (no comments, no explicit network topology); models user propagation with CNN and GRU, constructs fully-connected user interaction graphs weighted by feature similarity, applies GCN and dual co-attention mechanism to jointly highlight suspicious users and informative words; achieves 87.67% accuracy on Twitter15 and 90.84% on Twitter16 (18–20% improvement over prior work); demonstrates early detection at 90% accuracy with only 10 retweets; provides interpretable explanations of suspicious user characteristics and dramatic linguistic markers.

• Belief Evolution Network-based Probability Transformation and Fusion — 2021 · arXiv preprint

• Causal Understanding of Fake News Dissemination on Social Media — 2021 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks — 2020 · AAAI 2020

Bot detection

• Cresci et al. (2016) — DNA-Inspired Online Behavioral Modeling and Its Application to Spambot Detection — encodes user actions as character sequences and applies longest common substring analysis to detect groups of similar accounts; outperforms supervised and unsupervised baselines achieving MCC 0.952 on political bots; demonstrates paradigm shift toward group-level detection of evolved spambots
• The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the — empirical evidence of a new generation of social spambots that evade all existing detection approaches (Twitter, humans, academic tools); crowdsourced evaluation shows 0.24 accuracy vs 0.91 on traditional bots; paradigm shift toward group-level detection methods
• Better Safe Than Sorry: an Adversarial Approach to improve Social Bot Detection — proposes GenBot, a genetic algorithm for synthesizing evolved spambots; demonstrates that evolved bots evade state-of-the-art detection (F₁ ≈ 0.26) while revealing an actionable vulnerability (entropy-based signatures); introduces proactive detection paradigm to anticipate and preemptively defend against future bot evolutions
• A Decade of Social Bot Detection — decade-long longitudinal review (2010–2020) of social bot detection research; systematically catalogs 230+ detectors across two dimensions (individual vs. group detection, methodological approach); documents three "waves" of bot evolution and shift from individual-account to group-level detection approaches; analyzes publication trends showing exponential growth post-2014; argues modern ML detectors fail due to non-stationarity and non-neutrality assumptions
• Detection of Novel Social Bots by Ensembles of Specialized Classifiers — ensemble of specialized classifiers; shows heterogeneous bot behaviors (spammers, fake followers, political bots) are distinguished by different feature sets; ESC trains per-type classifiers combined via maximum rule; achieves 56% improvement in F1 score for novel bots (47% → 73%); improves cross-domain recall from 42% to 84%; enables efficient adaptation with fewer labeled examples for retraining; deployed in Botometer v4 achieving AUC 0.99
• Davis et al. (2016) — BotOrNot: A System to Evaluate Social Bots — publicly available web and API service for classifying Twitter accounts as human or bot using 1,000+ features; Random Forest classifier achieves 0.95 AUC on 15k bots and 16k legitimate accounts; served over one million API requests since 2014 launch
• Arming the public with artificial intelligence to counter social bots — comprehensive review of social bot types, activities, and impact; case study of Botometer bot detection tool; user experience survey (N=731) revealing interpretation challenges; proposes calibration methods (Platt scaling, Complete Automation Probability) to make bot scores interpretable; demonstrates model generalization across diverse bot types
• Scalable and Generalizable Social Bot Detection through Data Selection — scalable metadata-only bot detection framework using only 20 user features; achieves 900M tweets/day processing speed; compiles 13 labeled datasets (94K bots, 43K humans); demonstrates that strategic data selection (training on curated subset) improves generalization and consistency better than exhaustive training; achieves 0.99 AUC on unseen datasets
• Shao et al. (2018) — Anatomy of an online misinformation network — network analysis of fact-checking vs. misinformation diffusion during 2016 U.S. election; k-core decomposition reveals strong segregation between claim and fact-check communities; fact-checking nearly disappears in dense network cores dominated by bots and misinformation spreaders; identifies efficient node-removal strategies for disrupting misinformation circulation
• Shao et al. (2017) — The spread of low-credibility content by social bots — large-scale empirical analysis (14M messages, 400K articles) during 2016 U.S. election; shows only 6% of accounts spreading misinformation are bots but they account for 31% of tweet volume; bots employ early amplification strategy and target influential users; network dismantling analysis shows removing bots is critical for reducing misinformation spread
• Varol et al. (2017) — Online Human-Bot Interactions: Detection, Estimation, and Characterization — large-scale machine-learning framework extracting 1,150 behavioral features to classify bots from Twitter accounts; evaluates on 14M accounts; estimates 9–15% of active users are bots; clustering analysis reveals behavioral phenotypes; demonstrates concept drift in detection systems
• Ferrara et al. (2015) — The Rise of Social Bots — foundational survey of social bot phenomenon and detection methods; proposes taxonomy dividing approaches into graph-based detection (network structure), crowd-sourced detection (human judgment), and feature-based detection (behavioral patterns); analyzes characteristics distinguishing bots from humans and discusses arms race between sophistication and detection.

• Ayoobi, Shahriar & Mukherjee (2023) — The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention — Dataset of 3,600 LinkedIn profiles (1,800 legitimate, 600 human-created fake, 1,200 ChatGPT-generated) and Section and Subsection Tag Embedding (SSTE) method for detection; achieves 95% accuracy distinguishing legitimate from fake profiles, and 70%+ accuracy on unseen ChatGPT-generated profiles despite absence from training; demonstrates that minimal LLM-generated training samples suffice for generalization to diverse LLM outputs.

• Botometer 101: Social bot practicum for computational social scientists — 2022 · arXiv

• Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams — 2010 · arXiv

Media manipulation & coordinated campaigns

• Hosseinmardi et al. (2022) — Examining the consumption of radical content on YouTube — large-scale empirical audit (N=309.8K Americans, 2016–2019) of YouTube's role in radicalization; finds far-right content is small (0.05% of users) and stable; anti-woke content grew steadily; 55% of far-right views arrive via external URLs/search/homepage rather than recommendations; no within-session escalation toward extremity; on/off-platform consumption patterns align (2–3× co-consumption), suggesting user preference over algorithmic steering; concludes YouTube is one library in a larger ecosystem.
• Munger & Phillips (2022) — Right-Wing YouTube: A Supply and Demand Perspective — longitudinal platform analysis (2008–2018+) of 35 far-right YouTube channels (26K+ videos) vs. 219 mainstream media channels; identifies three creator clusters (Conservative, Alt-Lite, Alt-Right) with distinct ideological positioning and audience dynamics; demonstrates viewership peaked in 2017 and declined afterward, driven by supply-side affordances (low creation barriers, monetization, algorithmic recommendation) and demand-side audience appetite rather than algorithm amplification alone; reframes radicalization debate as supply-and-demand problem fundamental to platform affordance design.
• Ribeiro, Veselovsky & West (2023) — The Amplification Paradox in Recommender Systems — agent-based model resolving the contradiction between algorithmic audits (showing recommendation amplification of extreme content) and real user logs (showing recommendations don't drive extreme consumption); demonstrates that collaborative filtering plus content nicheness alone explain both observations; argues audits must model user utility preferences to meaningfully assess radicalization and filter bubbles; challenges regulatory frameworks treating all algorithmic prominence as "amplification."
• Stella, Ferrara & De Domenico (2018) — Bots increase exposure to negative and inflammatory content in online social systems — analysis of ~3.6M tweets during 2017 Catalan independence referendum; ~33% of active users are bots; shows bots from network periphery strategically target human influencers (Kendall τ=0.62, p<10⁻⁴); amplify group-specific inflammatory content (violent narratives for Independentists vs. neutral for Constitutionalists); demonstrate bot-driven social contagion reinforces and exacerbates human polarization.
• Bail et al. (2020) — Assessing the Russian Internet Research Agency's impact on the political attitudes and behaviors of American Twitter users in late 2017 — rare causal assessment of IRA's political impact; longitudinal study (N=1,239 partisan Twitter users) finding no significant effect of IRA interaction on political attitudes (affective/ideological) or behaviors (engagement, network composition) over one month; identifies users with strong echo chambers and high political interest as most likely to interact with IRA, suggesting Russians may have failed to polarize because they targeted already-entrenched partisans.
• Stukal et al. (2017) — Detecting Bots on Russian Political Twitter — large-scale bot detection on Russian political Twitter (2014–2015); ensemble supervised classifier achieves 95% precision and 87% recall; finds >50% of politically-active accounts are bots; software platform (Twitterfeed, ifttt.com) strongest bot predictor; bot activity spikes during Crimean annexation and opposition politician assassination (Nemtsov), suggesting coordinated propaganda use.
• Golovchenko et al. (2020) — Cross-Platform State Propaganda: Russian Trolls on Twitter and YouTube during the 2016 U.S. Presidential Election — analysis of IRA propaganda strategy (1,052 accounts, 108,781 tweets); examines hyperlinks to news media and YouTube videos; finds IRA linked to conservative sources 66% of the time; identifies "pre-propaganda" strategy where liberal-leaning accounts build credibility before amplifying conservative content; demonstrates cross-platform propaganda exploitation.
• Linvill & Warren (2020) — Troll Factories: Manufacturing Specialized Disinformation on Twitter — analysis of Russia's Internet Research Agency (IRA) operations on Twitter (2009–2018), identifying five specialized account categories (Right Troll, Left Troll, News Feed, Hashtag Gamer, Fearmonger) with distinct behavioral signatures; demonstrates IRA operated as coordinated "propaganda factory" with specialized units responding to political events (2016 election debates, Podesta email release); content analysis alone identifies account type in 83% of cases.
• Giglietto et al. (2020) — It takes a village to manipulate the media: coordinated link sharing behavior during 2018 and 2019 Italian elections — detects coordinated inauthentic behavior via near-simultaneous link sharing on Facebook; empirical evidence that coordinated networks amplify problematic domains 1.79–2.22× more than uncoordinated entities; distinguishes political vs. non-political deceptive networks.
• Lukito (2019) — Coordinating a Multi-Platform Disinformation Campaign: Internet Research Agency Activity on Three U.S. Social Media Platforms, 2015 to 2017 — empirical analysis of IRA's coordinated strategy across Facebook, Twitter, and Reddit (3,126 ads, 1.9M tweets, 12.6K posts); uses vector autoregression to demonstrate platform-aware temporal coordination; reveals Reddit as "trial balloon" for testing messages before Twitter amplification; shows IRA Twitter activity responds to Trump approval ratings.

• Machado et al. (2019) — A Study of Misinformation in WhatsApp groups with a focus on the Brazilian Presidential Elections — empirical study of 130 public WhatsApp groups during 2018 Brazilian election; analyzes 45,072 URLs and 400 media files; develops grounded typology; finds 13.1% of links from junk news sources, 40% to YouTube; documents strategic use of messaging platforms for campaign propaganda outside public platform oversight.

• Characterizing the Use of Images in State-Sponsored Information Warfare Operations — 2019 · AAAI Conference on Web and Social Media

• Who Let The Trolls Out? Towards Understanding State-Sponsored Trolls — 2018 · arXiv
• Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web — 2018 · arXiv

Fringe communities and internet culture

• Zannettou et al. (2018) — On the Origins of Memes by Means of Fringe Web Communities — large-scale empirical study of meme origins and propagation across Twitter, Reddit, /pol/, and Gab (160M images, 2016–2017); uses perceptual hashing and custom distance metrics to identify 12.6K meme clusters; employs Hawkes processes to quantify directed influence between communities; finds /pol/ and The_Donald substantially influence mainstream meme ecosystems despite modest size; documents disproportionate prevalence of hateful and anti-semitic memes on fringe communities.

• Examining the consumption of radical content on YouTube — 2021 · PNAS (Proceedings of the National Academy of Sciences)

• Auditing Radicalization Pathways on YouTube — 2019 · Proceedings of the ACM 2020 Conference on Fairness, Accountability, and Transparency

LLM-generated text detection

• Tang, Chuang & Hu (2023) — The Science of Detecting LLM-Generated Texts — comprehensive survey of black-box and white-box detection approaches for LLM-generated text, covering data collection, feature selection (statistical disparities, linguistic patterns, fact verification), classification models, watermarking strategies, benchmark datasets (HC3, Neural Fake News, etc.), and challenges including adaptive attacks, bias in training data, and threats from open-source LLMs.

• Yi et al. (2025) — Challenges and Innovations in LLM-Powered Fake News Detection — comprehensive survey of LLM applications to misinformation detection; synthesizes four frameworks (MiLk-FD, FND-LLM, DAFND, SheepDog) and identifies critical challenges: multimodal integration, adversarial robustness, scalability, interpretability, and LLM-specific issues (hallucination, semantic ambiguity, computational cost).

• Beyond Speculation: Measuring the Growing Presence of LLM-Generated Texts — 2026 · IEEE Computer

• Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detection — 2026 · World Wide Web Conference (WWW)
• Your AI Text is not Mine'': Redefining and Evaluating AI-generated Text — 2026 · arXiv preprint
• SINcon: Mitigate LLM-Generated Malicious Message Injection Attack for Rumor — 2025 · arXiv preprint
• DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection — 2024 · arXiv

• A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions — 2023 · arXiv

• Build it, Break it, Repeat: Benchmarking and improving LLM-manipulated disinformation — 2026 · arXiv cs.SI

• The GenAI Catch-22: Use of Generative Artificial Intelligence in Norwegian — 2026 · arXiv cs.CY

Style / content-based detection

• Wang & Chang (2022) — Toxicity Detection with Generative Prompt-based Inference — zero-shot toxicity detection using generative prompt-based classification; compares generative (estimating p(x|y)) vs. discriminative formulations; careful prompt engineering crucial for performance; demonstrates generative approach outperforms discriminative and embedding-similarity baselines on SBIC, HateExplain, and Civility datasets; qualitative analysis reveals LLMs sometimes rely on spurious correlations learned during pre-training.
• Oshikawa, Qian, & Wang (2020) — A Survey on Natural Language Processing for Fake News Detection — comprehensive NLP survey systematically comparing task formulations (classification vs. regression), nine benchmark datasets (LIAR, FEVER, FakeNewsNet, SNS data), and five methodological approaches (preprocessing, ML models, rhetorical approaches, evidence collection); demonstrates attention-based LSTM models outperform hand-crafted linguistic features; achieves 41.5–45.7% on LIAR, 68–76% on FEVER, 94.4% on FakeNewsNet (with graph convolutional networks).
• Singhania, Fernandez & Rao (2023) — 3HAN: A Deep Neural Network for Fake News Detection — three-level hierarchical attention network modeling articles at word, sentence, and headline-body levels. Word-level attention extracts relevant words; sentence-level attention identifies informative sentences; headline-body attention captures stance between headline and body. Achieves 96.77% with headline-based pre-training; provides interpretable attention visualizations showing which words and sentences drive fake news predictions.
• Liu, Wang, Li & Li (2024) — TELLER: A Trustworthy Framework For Explainable, Generalizable and Controllable Fake News Detection — dual-system framework combining LLM-driven cognition system (decomposes claims into interpretable yes/no questions) with neural-symbolic decision system (learns transparent logic rules via disjunctive normal form); achieves 76% accuracy on GossipCop and 80%+ on three datasets while maintaining explainability, generalizability, and human controllability; demonstrates integration of human expertise with machine learning for trustworthy detection.
• Potthast et al. (2017) — A Stylometric Inquiry into Hyperpartisan and Fake News — stylometric analysis via writing style features showing hyperpartisan news can be distinguished from mainstream (F1=0.78), satire from both (F1=0.81), but style-based fake news detection alone insufficient (F1=0.46); introduces Unmasking technique for assessing style similarity between text categories; corpus of 1,627 fact-checked articles from BuzzFeed.
• Karimi & Tang (2019) — Learning Hierarchical Discourse-level Structure for Fake News Detection — proposes HDSF framework that automatically learns discourse-level dependency trees (hierarchical sentence organizations) via inter-sentential attention; identifies three structure-related properties distinguishing fake/real news: leaf node count (coherence), preorder difference (sentence ordering), parent-child distance (discourse cohesion); achieves 82.19% accuracy, outperforming linguistic baselines; real news documents exhibit statistically significant higher coherence in discourse structures.
• Rashkin et al. (2017) — Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking — linguistic analysis of news across satire, hoax, propaganda, and trusted categories; demonstrates that fake news uses more first-person pronouns, superlatives, modal adverbs, and hedging while trusted news uses concrete language and assertive verbs; news reliability classification 65% F1; fine-grained truthfulness prediction on PolitiFact 6-point scale achieves 22% F1 (6-class) and 52% F1 (2-class).
• Zhou et al. (2023) — Linguistic-style-aware Neural Networks for Fake News Detection — HERO hierarchical recursive neural network; constructs per-document linguistic trees integrating constituency and RST discourse structure; Bi-GRU aggregation preserves global tree topology; attribute-specific variant 0.866/0.896 AUC on Recovery/MM-COVID, outperforming HAN, Text-GCN, DRNN, and Transformer baselines.
• Cao et al. (2025) — Is Less Really More? Fake News Detection with Limited Information — SLIM framework; replaces full article text with MMR-selected keywords, POS/NER sequence tags, or metadata; 30% keyword extraction achieves ~99% accuracy ratio vs. full text; XLNet_base backbone; 95.55%/97.60% accuracy on ReCOVery/Fake_And_Real_News.

• Kaliyar, Goswami & Narang (2021) — FakeBERT: Fake News Detection in Social Media with a BERT-based Deep Learning Approach — combines BERT embeddings with parallel 1D convolutional neural networks using varying kernel sizes for multi-scale feature extraction; achieves 98.90% accuracy on real-world 2016 U.S. Presidential Election dataset, substantially outperforming CNN (92.70%) and LSTM (97.55%) baselines, demonstrating the effectiveness of contextualized transformer embeddings for fake news detection on social media.

• A Heuristic-driven Uncertainty based Ensemble Framework for Fake News Detection — 2021 · arXiv preprint

• Perceived and Intended Sarcasm Detection with Graph Attention Networks — 2021 · arXiv
• Defending Against Neural Fake News — 2019 · NeurIPS (Neural Information Processing Systems)
• Combating Fake News: A Survey on Identification and Mitigation Techniques — 2018 · ACM Transactions on Intelligent Systems and Technology
• Fake News Detection on Social Media: A Data Mining Perspective — 2017 · arXiv
• Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation — 2017 · arXiv preprint

Knowledge & fact-checking

• Zhang et al. (2023) — How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances — Comprehensive survey of methods to keep LLMs aligned with ever-changing world knowledge without retraining; systematically categorizes implicit approaches (knowledge editing, continual learning) and explicit approaches (memory-augmented, retrieval-augmented, internet-enhanced); compares scalability, efficiency, and parameter-modification trade-offs; identifies open challenges in robust knowledge updates and unified evaluation benchmarks
• Quelle & Bovet (2023) — The Perils & Promises of Fact-checking with Large Language Models — Evaluates GPT-3.5 and GPT-4 for fact-checking on PolitiFact and 16+ languages in Data Commons; demonstrates evidence retrieval via Google Search improves accuracy by 10–20 percentage points; reveals critical language-specific performance disparities with non-English claims underperforming when compared to English translations, suggesting fundamental training-data bias in multilingual verification
• Asai et al. (2023) — Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection — extends RAG by teaching language models to adaptively decide when to retrieve, and to generate reflection tokens for self-critique; achieves superior performance on PubHealth, PopQA, ASQA, and biography generation; demonstrates improved factuality and citation accuracy.
• Dun et al. (2021) — KAN: Knowledge-aware Attention Network for Fake News Detection — incorporates entities and their knowledge graph contexts (immediate neighbors in Wikidata) via entity linking; applies News-towards-Entities (N-E) and News-towards-Entities-and-Entity-Contexts (N-E2C) attention mechanisms to weight entity importance; achieves 7.4% F1 improvement on PolitiFact, 2.8% on GossipCop, 9.7% on PHEME over prior knowledge-graph methods.
• Mayank, Sharma & Sharma (2021) — DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection — combines biLSTM news encoding with Wikidata knowledge graph embeddings (ComplEx) to detect fake news from titles alone; achieves 88% and 78% F1 on two datasets through systematic bias removal and entity-based feature integration.
• Ciampaglia et al. (2015) — Computational fact checking from knowledge networks — knowledge-graph-based approach framing fact checking as shortest-path computation in Wikipedia; semantic proximity metric based on entity generality enables high-accuracy verification; demonstrates that graph topology carries truth signal independent of text content.
• Nieminen & Rapeli (2019) — Fighting Misperceptions and Doubting Journalists' Objectivity: A Review of Fact-checking Literature — comprehensive literature review of 48 studies on political fact-checking, examining effectiveness in reducing misperceptions (mixed results including backfire effects), fact-checking as a profession (methodological inconsistencies), and public opinion about fact-checking; documents that 88% of studies are US-focused.

• Graves (2016) — Boundaries Not Drawn: Mapping the institutional roots of the global fact-checking movement — ethnographic mapping of the global fact-checking movement's organizational landscape across journalism, academia, and politics/civil society axes; identifies institutional diversity and contested professional boundaries across countries.

• Explainable Claim Verification via Knowledge-Grounded Reasoning with Large — 2023 · arXiv

• Where Are the Facts? Searching for Fact-checked Information to Alleviate the — 2020 · arXiv preprint
• Finding Streams in Knowledge Graphs to Support Fact Checking — 2017 · arXiv preprint

Evidence-based detection

• Popat et al. (2018) — DeClareE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning — end-to-end neural network combining claims and evidence articles via bidirectional LSTMs with claim-specific attention; automatically discovers which web articles support or refute a claim without hand-crafted features; 78.96% accuracy on Snopes, demonstrating the value of external evidence for credibility assessment.
• Vo & Lee (2021) — Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection — proposes MAC with hierarchical multi-head attention at both word and document levels; word-level attention identifies important phrases in claims and evidence; document-level attention weights evidence sources by relevance; jointly optimized with BiLSTM embeddings; achieves 88.7% AUC on Snopes (9.47% improvement over baselines) and 75.8% on PolitiFact; ablation studies demonstrate both attention levels are essential for evidence-aware fact-checking.
• Jin et al. (2021) — Towards Fine-Grained Reasoning for Fake News Detection — constructs claim-evidence graphs from social media (posts, users, keywords) and uses mutual-reinforcement-based ranking to identify salient evidence; proposes bi-channel kernel graph attention network integrating textual and social signals for fine-grained reasoning; achieves 91.7% F1 on PolitiFact and 86.4% F1 on GossipCop with interpretable explanations of which evidence groups matter most for each prediction.

Multimodal

• Yan et al. (2025) — Debunk and Infer: Multimodal Fake News Detection via Diffusion-Generated Evidence and LLM Reasoning — DIFND framework combining diffusion-based evidence generation with multi-agent LLM reasoning for fake news video detection. Proposes Debunk Diffusion, a conditional latent diffusion model that generates refuting/authenticating textual evidence from multimedia content. Deploys multiple MLLMs (Deepseek-R1, GLM-4-Voice) as collaborative agents analyzing text, audio, and video independently, then fusing outputs via attention mechanisms. Achieves 87.68% accuracy and 87.34% F1 on FakeSV; 92.16% accuracy and 92.01% F1 on FVC, with 1.89–2.88% improvements over prior multimodal baselines through integration of generated evidence and structured reasoning.
• Hu, Hu & Zhang (2025) — Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection — Framework combining LLM-generated pseudo labels with graph-based label propagation for multimodal detection. Proposes Global Label Propagation Network with LLM-based Pseudo Labeling (GLPN-LLM) that integrates CLIP multimodal features with GCN-based propagation over cross-modal graphs. Introduces mask-based global label propagation to prevent label leakage while enabling effective signal propagation. Achieves 88.83% accuracy on Twitter, 86.47% on PHEME, 86.74% on Weibo, substantially outperforming naive LLM integration approaches.
• Jagtap et al. (2021) — Misinformation Detection on YouTube Using Video Captions — applies pre-trained word embeddings (GloVe, Word2Vec) to YouTube video captions for three-class (Misinformation, Debunking, Neutral) and binary classification; achieves 0.85–0.90 F₁ (three-class) and 0.92–0.95 F₁ (binary); demonstrates that video metadata (views, likes) alone insufficient but caption analysis with classical ML classifiers outperforms baselines across five conspiracy topics (vaccines, 9/11, chemtrails, moon landing, flat earth).
• Alam et al. (2021) — A Survey on Multimodal Disinformation Detection — comprehensive survey of multimodal disinformation covering text, images, speech, video, network structure, and temporal information; distinguishes factuality (content falsity) from harmfulness (intent to deceive/harm); systematically reviews ~140 papers on detection approaches and identifies key challenges in combining multiple modalities.
• Nakamura, Levy & Wang (2019) — r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection — large-scale multimodal dataset with 1.06M Reddit submissions (64% text+image) labeled for 2-way, 3-way, and 6-way classification; demonstrates multimodal models (BERT + ResNet50) achieve 85.88% 6-way accuracy, ~10 percentage points above text-only baselines; identifies satire and imposter content as hardest categories.
• Wang et al. (2018) — EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection — adversarial learning to remove event-specific features and learn event-invariant representations; feature extractor cooperates with fake news detector and tries to fool event discriminator; 71.5% / 82.7% accuracy on Twitter / Weibo; first to formulate fake news detection on newly emerged events as a transfer learning problem.
• Yang et al. (2018) — TI-CNN: Convolutional Neural Networks for Fake News Detection — parallel CNN branches for text and image; extracts explicit features (word counts, punctuation, capital letters, negations, pronouns, face count, resolution) and learns latent representations; concatenates both feature sets for classification; achieves F₁ 0.9210 on 2016 US presidential election news, significantly outperforming text-only (0.8920) and image-only (0.4729) approaches.
• Khattar et al. (2019) — MVAE: Multimodal Variational Autoencoder for Fake News Detection — variational autoencoder learning shared text-image representations; jointly trains encoder-decoder (VAE reconstruction) with binary classifier; 74.5% / 82.4% accuracy on Twitter / Weibo, improving ~6% over attention-based baselines by explicitly modeling cross-modal correlations.
• A Multi-Modal Method for Satire Detection using Textual and Visual Cues: Multi-modal satire detection using ViLBERT (Vision & Language BERT) on headline-image pairs from satirical and mainstream news sources; achieves 93.80% accuracy on 10,000-article dataset; demonstrates that early fusion and multi-modal pre-training outperform uni-modal and simple fusion baselines; notably, image forensics (ELA+CNN) alone underperforms, highlighting importance of joint reasoning.
• Zhou et al. (2020) — SAFE: Similarity-Aware Multi-Modal Fake News Detection — proposes cross-modal text-image similarity as a detection signal; modified cosine similarity between Text-CNN text and image2sentence visual representations; F₁ 0.896/0.895 on PolitiFact/GossipCop, outperforming text-only and prior multi-modal baselines.
• Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News: First to address defending against machine-generated multimodal fake news with images and captions. Proposes DIDAN, a named entity-based approach to detect visual-semantic inconsistencies by measuring named entity co-occurrence between article text and image captions. Introduces NeuralNews dataset of 128K articles across four types (real/generated articles × real/generated captions). Shows naive humans achieve only 46.2% detection accuracy while trained humans with visual-semantic cues reach 67.8%; identifies Type C (generated text + real images) as most deceptive.
• Silva et al. (2021) — Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multimodal Data — addresses practical problem that multimodal models trained on one domain fail on others (politics→entertainment, politics→COVID-19); unsupervised domain discovery via propagation network community detection; supervised domain-agnostic classifier preserves both domain-specific and cross-domain knowledge via dual decoders with adversarial loss; LSH-based instance selection reduces labeling cost; 7.55% F₁ improvement on rarely-appearing domains; 0.836–0.869 F₁ across PolitiFact/GossipCop/CoAID.
• Zhou et al. (2020) — ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research — multimodal COVID-19 news credibility dataset; 2,029 articles from 60 screened publishers with NewsGuard/MBFC labels, 140,820 tweets; benchmarks LIWC, RST, Text-CNN, and SAFE with SAFE achieving best F₁ 0.833/0.672.

• Yang et al. (2020) — CHECKED: Chinese COVID-19 Fake News Dataset — first Chinese-language COVID-19 misinformation dataset; 2,104 Weibo microblogs with per-item expert labels, multimedia, and 1.87M repost/1.19M comment propagation graphs; TextCNN baseline macro F₁ = 0.938.

• Agentic Mixed-Source Multi-Modal Misinformation Detection with Adaptive Test-Time — 2026 · ACM Conference

• Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal — 2026 · ICLR 2026

• Multimodal Emergent Fake News Detection via Meta Neural Process Networks — 2021 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining

• From Manipulation to Mistrust: Explaining Diverse Micro-Video Misinformation — 2026 · ACM International Conference

• What's Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal — 2026 · arXiv

Source / social context

• Baly et al. (2019) — Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media — applies Copula Ordinal Regression to jointly model outlet trustworthiness (3-point scale) and political ideology (7-point scale); auxiliary tasks at different bias granularities (binary, 3-point, 5-point) reduce prediction error; factuality MAE 0.481, bias MAE 1.479; demonstrates that extreme bias and low factuality are correlated.
• Nakov et al. (2021) — A Survey on Predicting the Factuality and the Bias of News Media — comprehensive survey of media-level profiling for source reliability and political bias prediction; reviews textual features (linguistic markers, embeddings), multimedia analysis (image/video forensics), audience homophily (follower ideology), and infrastructure characteristics (domain, hosting); argues for joint factuality-bias modeling; documents that bias prediction is easier than factuality because factuality requires external ground-truth verification.
• Shu, Wang & Liu (2019) — Beyond News Contents: The Role of Social Context for Fake News Detection — tri-relationship embedding framework jointly modeling publisher-news relations and user-news interactions; incorporates publisher partisan bias and user credibility; achieves 4–6% F1 improvement over content+profile baselines on FakeNewsNet; demonstrates early detection (>80% F1 within 48 hours).
• Eady et al. (2019) — How Many People Live in Political Bubbles on Social Media? Evidence From Linked Survey and Twitter Data — links survey data (N=1,496) to Twitter behavioral data (642K accounts, 1.2B tweets); measures ideological exposure through following and retweet patterns; finds substantial cross-ideological overlap, contradicting "filter bubble" narratives; only 34% of most-conservative quintile live in extreme partisan bubbles; retweets and weak ties provide important bridges across ideological divides; documents asymmetries with conservatives more likely to follow left-leaning outlets.
• Grinberg et al. (2019) — Fake news on Twitter during the 2016 U.S. presidential election — individual-level Twitter analysis linked to voter registration; extreme concentration of fake news consumption (1% of users see 80%) driven by older, conservative, politically engaged subset; identifies distinct media ecosystem cluster for fake news with high internal density; shows mainstream sources still dominant even for extreme-right users.
• Mosleh et al. (2021) — Shared partisanship dramatically increases social tie formation in a Twitter field experiment — Twitter field experiment with bot accounts; users 3× more likely to reciprocally follow copartisans (β=0.093, p<0.001); no partisan asymmetry (Democrats and Republicans equally homophilous); demonstrates partisan preference as intrinsic driver of echo chamber formation, not purely algorithmic; n=842, preregistered.
• Mosleh et al. (2021) — Cognitive reflection correlates with behavior on Twitter — field study linking Cognitive Reflection Test scores to actual Twitter behavior; high-CRT users are more discerning: follow fewer accounts, share higher-quality news sources, show cognitive echo chambers independent of partisanship; N=1,901 from Prolific with Twitter API data.
• Shu et al. (2019) — The Role of User Profiles for Fake News Detection — characterizes Twitter user profiles (explicit + implicit) of fake vs. real news sharers; UPF vector outperforms RST and LIWC content baselines on FakeNewsNet.
• Shu et al. (2019) — dEFEND: Explainable Fake News Detection — jointly encodes news sentences and user comments via hierarchical attention with sentence-comment co-attention; outperforms content-only (HPA-BLSTM) and comment-only baselines; demonstrates complementarity of news and social signals for both detection and explainability.

• Sitaula et al. (2019) — Credibility-based Fake News Detection — frames detection as credibility assessment; 3 source features (author count + publication history) achieve F1-macro 0.77–0.83, outperforming 23 content features (0.68) on PolitiFact + BuzzFeed.

• Network Information Enhances Unreliable News Domain Detection — 2026 · arXiv cs.SI

Susceptibility factors and public health behavior

• Suarez-Lledo & Alvarez-Galvez (2021) — Prevalence of Health Misinformation on Social Media: Systematic Review — systematic review of 69 studies characterizing health misinformation topics and their prevalence across social media platforms. Vaccines (32% of studies), drugs/smoking (22%), noncommunicable diseases (19%), pandemics (10%), eating disorders (9%), and medical treatments (7%) emerge as principal categories; health misinformation most prevalent on Twitter; provides standardized measurement framework and identifies key analytical approaches for future research.
• Jones-Jang, Mortensen & Liu (2021) — Does Media Literacy Help Identification of Fake News? Information Literacy Helps, but Other Literacies Don't — national survey (N=1,299) testing four literacy types (media, information, news, digital) as predictors of fake news identification ability. Only information literacy—the ability to navigate, search, and evaluate verified information sources—significantly predicts accurate identification; media, news, and digital literacies showed no significant relationship, challenging common assumptions about media literacy interventions.
• Vraga & Tully (2021) — News literacy, social media behaviors, and skepticism toward information on social media — national survey (N=788) examining how news literacy, cognitive reflection, self-perceived media literacy, and value for media literacy relate to exposure and posting of political content on social media, and whether they predict skepticism toward information encountered in these spaces; finds actual knowledge (news literacy) predicts less engagement but greater skepticism, while self-assessed literacy shows opposite pattern; platform-specific effects with news literacy suppressing engagement on Facebook and YouTube but not Twitter.

• Roozenbeek et al. (2020) — Susceptibility to misinformation about COVID-19 around the world — international survey of 3,750 adults across five countries (UK, Ireland, USA, Spain, Mexico) examining psychological and cognitive predictors of COVID-19 misinformation susceptibility. Higher trust in scientists and numeracy predict lower misinformation belief; susceptibility predicts vaccine hesitancy and reduced health-guidance compliance across all countries.

• From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake — 2024 · arXiv

• Shifting attention to accuracy can reduce misinformation online — 2021 · Nature

Moral and behavioral psychology of misinformation

• Effron & Raj (2020) — Misinformation and Morality: Encountering Fake-News Headlines Makes Them Seem Less Unethical to Publish and Share — four preregistered experiments (N=2,587) demonstrating that repeated exposure to fake-news headlines reduces moral condemnation of spreading them independent of belief; fluency-driven moral desensitization operates via "familiar = acceptable" intuition; effect persists when headlines labeled false and mediates downstream sharing intentions on social media.

• Brady et al. (2017) — Emotion shapes the diffusion of moralized content in social networks — 563,312 tweets across gun control, same-sex marriage, climate change; moral-emotional words increase retweet rate ~20% per word; effect stronger within ideological in-groups than out-groups, explaining "echo chamber" polarization.

• Lazy, not biased: Susceptibility to partisan fake news is better explained

Adversarial & defensive

• Mazurczyk, Lee & Vlachos (2023) — Disinformation 2.0 in the Age of AI: A Cybersecurity Perspective — position paper proposing that disinformation should be treated as a cybersecurity threat, especially as AI enables large-scale creation and dissemination; introduces four attack scenarios (AI-generated content, perturbation, detection evasion, optimized spread) and proposes defense-in-depth countermeasures across social network, ISP, device, and user layers
• Hameleers et al. (2020) — A Picture Paints a Thousand Lies? — experimental evidence (N=1,404) that multimodal disinformation (text+image Twitter posts) is perceived as more credible than text-only falsehoods, but fact-checkers effectively reduce credibility regardless of modality; motivated reasoning moderates effectiveness—fact-checkers most persuasive when reaching those already skeptical of the false claim.
• Walter et al. (2020) — Fact-Checking: A Meta-Analysis of What Works and for Whom — meta-analysis of 30 studies quantifying fact-checking effectiveness (d = 0.29) and identifying key moderators including motivated reasoning, political ideology, message design, and context; pro-attitudinal corrections much more effective than counter-attitudinal ones; visual elements often backfire; campaign messaging harder to correct.
• Krause et al. (2020) — Fact-checking as risk communication: the multi-layered risk of misinformation in times of COVID-19 — applies 40+ years of risk communication research to COVID-19 fact-checking; argues that fact-checking fails without trust, and that competing definitions of misinformation risk across political groups undermine fact-checker credibility; recommends trust-building partnerships, transparency on uncertainty, and value-aligned messaging.
• Pennycook et al. (2020) — Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention — two pre-registered experiments (1,700+ participants) demonstrating that simple accuracy-salience nudge (rating an unrelated headline for accuracy) nearly triples truth discernment when deciding what to share; intervention works uniformly across partisanship, education, and geographic proximity to COVID-19 epicenters.

• Bode & Vraga (2015) — In Related News, That Was Wrong: The Correction of Misinformation Through Related Stories Functionality in Social Media — experimental test of Facebook's "related stories" feature for correcting health misinformation; shows algorithmic curation can reduce misperceptions when debunking matches user's initial position, with effects moderated by motivated reasoning

• Universal and Transferable Adversarial Attacks on Aligned Language Models — 2023 · ArXiv

• TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial — 2020 · arXiv

Early detection

• Zhou et al. (2020) — Fake News Early Detection: An Interdisciplinary Study — theory-driven feature engineering at lexicon-, syntax-, semantic-, and discourse-level; ≈89% accuracy on PolitiFact/BuzzFeed with content-only features; outperforms propagation-based and hybrid baselines; reveals empirical links between fake news, deception, and clickbait.

Explainability

• Danilevsky et al. (2020) — A Survey of the State of Explainable AI for Natural Language Processing in arXiv — comprehensive survey of explainability in NLP across 50 papers; categorizes explanations by local/global scope and self-explaining/post-hoc generation; details five core techniques (feature importance, surrogate models, example-driven, provenance-based, induction); reviews visualization methods (saliency, raw declarative, natural language); identifies evaluation gaps and future directions; foundational reference for interpretable NLP systems including fake-news detectors.

• Shu et al. (2019) — dEFEND: Explainable Fake News Detection — hierarchical attention networks jointly encoding news content and user comments; sentence-comment co-attention identifies which sentences and comments drive the fake-news prediction; 0.904 accuracy on PolitiFact; human evaluation demonstrates dEFEND ranks check-worthy sentences better than HPA-BLSTM.

• Controlled Change (2024)

• Discovering Latent Knowledge in Language Models Without Supervision — 2022 · arXiv preprint

Cross-domain & transfer

• Nan et al. (2021) — MDFEND: Multi-domain Fake News Detection — Introduces Weibo21, the first multi-domain fake news dataset from a single platform with 9 domains; proposes MDFEND using mixture-of-experts with domain gate to adaptively aggregate representations across domains; achieves 0.9137 F₁, outperforming single-domain and multi-domain baselines; directly addresses domain shift in linguistic patterns and propagation behavior.

• E-FreeM2: Efficient Training-Free Multi-Scale and Cross-Modal News Verification — 2025 · SCID '25 (2nd Workshop on Security-Centric Strategies for Combating Information

LLMs & generative-era

Real-world GenAI misuse and threat assessment: - Marchal et al. (2024) — Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data — First empirical taxonomy of real-world GenAI misuse based on 191 documented media incidents (Jan 2023–Mar 2024); identifies 18 distinct tactics across exploitation of GenAI capabilities (impersonation, falsification, content scaling) and technical system attacks; finds most misuse is low-tech and accessible, driven by five goals (opinion manipulation 27%, monetization 21%, fraud 18%, harassment 6%, reach 3.6%); demonstrates GenAI has democratized previously costly tactics for broader pools of actors with minimal technical expertise.

Empirical detection studies: - Su, Cardie & Nakov (2023) — Adapting Fake News Detection to the Era of Large Language Models — Comprehensive evaluation of fake news detectors across three stages: human-written dominance (Human Legacy), mixed human-machine (Transitional Coexistence), and machine-generated dominance. Key finding: detectors trained exclusively on human-written fake news generalize poorly to machine-generated fakes. Recommends training on balanced human-machine data to improve robustness. Benchmarks RoBERTa, BERT, ELECTRA, ALBERT, DeBERTa on GossipCop++ and PolitiFact++ datasets; reveals data distribution shifts caused by LLMs create asymmetric generalization challenges. - Dugan et al. (2022) — Real or Fake Text?: Boundary Detection — Investigates human ability to detect transition points where text shifts from human-written to machine-generated (boundary detection). Introduces RoFT game platform; 21,000+ annotations across four genres show humans achieve 23.4% on first guess (vs. 10% random) and 72.3% with top-3 guesses; larger models harder to detect; genre-specific error patterns; monetary incentives improve learning. - Can LLM-Generated Misinformation Be Detected? — Empirical evidence that LLM-generated misinformation is harder to detect for humans (9.6% vs 40.7% success) and detectors than human-written content with same semantics; builds taxonomy and LLMFake dataset.

Comprehensive surveys: - Combating Misinformation in the Age of LLMs: Opportunities and Challenges — Systematic review of both opportunities (detection, intervention, attribution) and challenges (hallucination, intentional generation) for using LLMs in misinformation research; examines domain-specific threats and countermeasures.

Model evaluation and understanding:

Model alignment and instruction-following: - Askell et al. (2021) — A General Language Assistant as a Laboratory for Alignment — interactive evaluation framework for alignment using helpfulness, honesty, and harmlessness (HHH) criteria; compares prompting, imitation learning, binary discrimination, and ranked preference modeling; finds ranked preference modeling scales better than imitation learning; introduces preference model pre-training (PMP) on public data to improve sample efficiency.

Disinformation generation and safety: - Toxicity in ChatGPT: Analyzing Persona-assigned Language Models — Large-scale systematic analysis of persona-induced toxicity in ChatGPT; shows safety mechanisms can be bypassed via system parameter manipulation; 6× toxicity increase possible; reveals discriminatory bias targeting certain demographic groups, countries, and entity categories - Vykopal et al. (2023) — Disinformation Capabilities of Large Language Models — Comprehensive empirical study of 10 LLMs' ability to generate disinformation news articles across 20 narratives (COVID-19, Russia-Ukraine, health, elections); most models readily agree with dangerous claims; Falcon is sole exception with effective safeguards; ChatGPT shows behavioral safety; existing detectors achieve ~0.8 F1 but struggle per-sample.

Truthfulness and hallucination evaluation: - Ji et al. (2022) — Survey of Hallucination in Natural Language Generation — comprehensive survey of hallucination across six major NLG tasks (abstractive summarization, dialogue generation, QA, data-to-text generation, machine translation, vision-language generation) and LLMs; defines intrinsic and extrinsic hallucinations; reviews metrics (statistical, model-based, human evaluation) and mitigation methods (architecture, training, post-processing, controllable generation); identifies task-specific tolerance differences and open challenges. - Lin, Hilton & Evans (2021) — TruthfulQA: Measuring How Models Mimic Human Falsehoods — benchmark demonstrating larger language models are less truthful; proposes automated metric for evaluating factual accuracy.

Robustness of detectors and adversarial attacks: - Sadasivan et al. (2023) — Can AI-Generated Text be Reliably Detected? — Comprehensive stress-testing of four detector classes (watermarking, neural network-based, zero-shot, retrieval-based) using recursive paraphrasing attacks; reduces watermark detector AUROC from 99.8% to 80.7%, and retrieval-based detectors below 60% accuracy with only modest text quality degradation; establishes theoretical bound on detector AUROC via total variation distance between text distributions, revealing fundamental hardness as LLMs improve. - Mao et al. (2024) — RAIDAR: Generative AI Detection via Rewriting — Detection method leveraging rewriting behavior: LLMs preserve their own generated text while modifying human-written text when asked to rewrite. Measures three structural properties (invariance, equivariance, output uncertainty) from editing distance without requiring internal model access. Achieves 60–95 F1 across diverse domains (news, essays, code, reviews, arXiv) with 29-point improvements over prior methods; robust to adversarial rephrasing attacks even when adversaries know the detection mechanism (up to 93 F1).

Zero-shot detection methods: - Mitchell et al. (2023) — DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature — Identifies that LLM-generated text occupies negative-curvature regions of the log-probability landscape; zero-shot method using random perturbations to estimate Hessian trace; achieves 0.95 AUROC on GPT-2 detection without training data or access to model parameters.

Generation attacks and detection: - Adelani et al. (2019) — Generating Sentiment-Preserving Fake Online Reviews — Demonstrates practical attack using fine-tuned GPT-2 to generate high-quality product reviews; two-step approach (generation + BERT validation) preserves sentiment; shows humans and automated detectors (Grover, GLTR, OpenAI detector) struggle to distinguish generated from authentic reviews; sentiment preservation rates 67–71% with fine-tuning. - Ippolito et al. (2019) — Automatic Detection of Generated Text is Easiest when Humans are Fooled — empirical study contrasting human and automatic detection of GPT-2-generated text across three decoding strategies (top-k, nucleus sampling, untruncated random). Fine-tuned BERT achieves 80%+ accuracy on long (192-token) excerpts versus 71% for trained human raters. Critical finding: detectors trained on one strategy transfer poorly to others (42.5% accuracy drop when trained on top-k and tested on nucleus), whereas humans remain robust. Reveals asymmetric difficulty: detection is easiest when humans are fooled (nucleus sampling) but hardest when humans remain reliable (top-k). - Clark et al. (2021) — All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text — large-scale study (1,170 Amazon Mechanical Turk evaluators) examining humans' ability to assess machine-generated text from GPT2 and GPT3 across three domains (stories, news, recipes). Untrained evaluators achieve 57% accuracy on GPT2 and 50% (chance) on GPT3; three training interventions (instructions, examples, comparisons) improve accuracy modestly, with example-based training reaching 55% overall. Reveals evaluators focus on surface-level features (grammar, spelling, style) rather than content; provides evidence that consistent human evaluation methodology is critical for benchmarking NLG quality and detection systems.

Misinformation generation and ODQA poisoning: - On the Risk of Misinformation Pollution with Large Language Models — Investigates LLM-generated misinformation threat to ODQA systems; demonstrates GPT-3.5 can generate credible false passages that degrade retrieval-based QA performance (14–87% EM drop); proposes misinformation detection, vigilant prompting, and reader ensemble defenses

• A Survey of Large Language Models — 2023 · arXiv
• A Watermark for Large Language Models — 2023 · arXiv preprint
• Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity — 2023 · arXiv
• Language Models are Few-Shot Learners — 2020 · NeurIPS 2020

Real-world impacts & polarization

• Garimella et al. (2017) — Quantifying Controversy on Social Media — three-stage graph-based pipeline for measuring controversy in social media discussions via conversation topology; proposes multiple network-structure metrics with random-walk-based approach (RWC) most reliably separating controversial from non-controversial topics; validates on Twitter and external datasets.
• Garimella et al. (2016) — Reducing Controversy by Connecting Opposing Views — algorithmic approach to mitigating echo chambers and polarization via graph-based edge recommendation; uses RWC metric to identify which edges to add; efficient algorithm (ROV) focuses on high-degree hub nodes; extends to ROV-AP incorporating acceptance probability based on user polarity; empirical validation on 10 Twitter controversy datasets.
• Cinelli et al. (2021) — The echo chamber effect on social media — comparative analysis of 100+ million posts across Twitter, Facebook, Reddit, and Gab; quantifies echo chambers via homophily in interaction networks and bias in information diffusion; shows platform architecture (feed algorithms vs. community-based curation) determines whether homophilic clustering and polarized diffusion emerge; Facebook and Twitter exhibit strong echo chambers while Reddit shows reduced segregation despite polarization.
• Bail et al. (2018) — Exposure to opposing views on social media can increase political polarization — field experiment on Twitter showing that repeated exposure to opposing political ideology can increase polarization (backfire effect), particularly for Republicans; challenges assumption that "breaking echo chambers" reduces polarization.
• Soares, Recuero & Zago (2018) — Influencers in Polarized Political Networks on Twitter — social network analysis of Twitter conversations during Brazil's 2016 impeachment process; identifies three influencer types (opinion leaders, informational influencers, activists) and shows that user behavior—especially activist retweeting of in-group messages—actively reinforces echo-chamber structure and polarization beyond algorithmic curation.
• Wilson & Wiysonge (2020) — Social media and vaccine hesitancy — large-scale cross-national study (137–166 countries) demonstrating causal links between social media activity and public health outcomes; social media organization for offline action predicts vaccine safety skepticism (cross-sectional); foreign disinformation campaigns associated with 2-percentage-point drop in vaccination coverage year-over-year; 15% increase in negative vaccine tweets per point on disinformation scale

• Mills et al. (2023) — Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media — pre-registered field experiment comparing Twitter's engagement-based ranking algorithm with reverse-chronological and stated-preference baselines; finds engagement-based ranking amplifies partisan (0.24 SD), emotionally charged, and out-group hostile content beyond what users report preferring; proposes and evaluates stated-preference ranking that reduces harmful amplification while maintaining engagement and satisfaction.

• Quantifying Political Partisanship for Cross-Platform Analyses — 2026 · arXiv cs.SI

Authors

See all authors — sorted alphabetically.

• Snidal Abbott
• Wael AbdAlmageed
• Alberto Acerbi
• Sibel Adalı
• Lada A. Adamic
• David Ifeoluwa Adelani
• Ehsan Adeli
• Darius Afchar
• Sadia Afroz
• Sandhini Agarwal
• Ranit Aharonov
• Wei Ai
• Oluwaseun Ajao
• Ahmet Aker
• Md Shad Akhtar
• Firoj Alam
• Michael Albright
• Dimitar Alexandrov
• Ahmed Ali
• Max Aliapoulios
• Hunt Allcott (New York University)
• Jennifer Allen
• Diogo Almeida
• Virgilio A. F. Almeida
• Javier Alvarez-Galvez
• Duarte M. Alves
• Pratuat Amatya
• Michelle A. Amazeen
• Mia Ameen
• Dario Amodei
• Jisun An
• Ankesh Anand
• Sophia Ananiadou
• Dimo Angelov
• Sinan Aral (MIT)
• Antonio A. Archer
• Antonio A. Arechar
• Lisa P. Argyle
• Arnav Arora
• Katya Artemova
• Akari Asai
• Amanda Askell
• John Aslanides
• Yash Kumar Atri
• Isabelle Augenstein
• Tal August
• Shahar Avin
• Marco Avvenuti (University of Pisa)
• Navid Ayoobi
• Zhongjie Ba
• Alireza Bagheri
• Yuntao Bai
• Christopher A. Bail
• Sriram Balasubramanian
• Adam Bales
• Cameron Ballard
• Gabriele Ballarin (Independent researcher)
• Avital Balwit
• Ramy Baly
• Juan M Banda
• Yejin Bang
• Maria Bannert
• Albert-László Barabási
• Alberto Barrón-Cedeño
• Ayan Basak
• Syed Raza Bashir
• Sejuti Basu
• Matthew Baum
• Becca Beets
• Andriy Bega
• Elizabeth Belding
• Irad Ben-Gal (Tel Aviv University)
• Yochai Benkler
• Carl Bergstrom
• Adam Berinsky
• H. Russell Bernard
• Christopher Berner
• Janek Bevendorff
• Michele Bevilacqua
• Tian Bian
• Maria Bielikova
• Battista Biggio
• Alexandra Birch
• Yonatan Bisk
• Joanna Bitton
• Jeremy Blackburn
• Jason Blazakis
• Leticia Bode
• Elizabeth Bodine-Baron
• Johan Bollen
• Rishi Bommasani
• Richard Bonneau
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• Claire Wardle (First Draft; Harvard Kennedy School)
• Greta Warren
• Patrick L. Warren (Clemson University)
• Zeerak Waseem
• Duncan J. Watts
• Alexander Wei
• Gerhard Weikum
• Jochen Weller
• Max Welling
• Ji-Rong Wen
• Yuxin Wen
• Zhengqi Wen
• Jevin West
• Robert West
• Michael Wiegand
• Adam Wierzbicki
• Hywel T. P. Williams
• Julian A. Wills
• Peter Wills
• Steven Lloyd Wilson
• Zev Winkelman
• Clemens Winter
• Charles Wiysonge
• Sean P. Wojcik
• Derek Fai Wong
• Kam-Fai Wong
• Geraldine Wong Sak Hoi
• Jeff Wu
• Jeffrey Wu
• Jiaying Wu
• Jindi Wu (Syracuse University)
• Junchao Wu
• Shu Wu
• Shuang Wu
• Yuan Wu
• Zeqiu Wu
• Congying Xia
• Chaowei Xiao
• Xi Xiao
• Xiaoming Zhang
• Xing Xie
• Han Xu
• Junjie Xu
• Rachel Xu
• Tingyang Xu
• Xovee Xu
• Yan Xu
• Zeqiu Xu
• Guangxu Xun
• Junichi Yamagishi
• Kaiying Yan
• Rui Yan
• Zhiling Yan
• Chen Yang (Syracuse University)
• Junfeng Yang
• Kai-Cheng Yang
• Kaicheng Yang
• Linyi Yang
• Liu Yang
• Min Yang
• Qiang Yang
• Ruichao Yang
• Shu Yang
• Wenming Yang
• Xin Yang
• Yang Yang
• Yifei Yang
• Ziang Yang
• Yuanshun Yao
• Yunzhi Yao
• Andrew Yates
• Haotian Ye
• Wei Ye
• Jingyuan Yi
• Xiaoyuan Yi
• Xin Yi
• Wen-tau Yih
• Hongzhi Yin
• Junwei Yin
• Lijun Yin
• Xunjian Yin
• Jin Yong Yoo
• Seunghyun Yoon
• Jingyuan Yu
• Kun Yu (Tsinghua University)
• Peiyang Yu
• Philip S. Yu
• Philip S. Yu
• Seunghak Yu
• Tiezheng Yu
• Yangyang Yu
• Wei Yuan
• Xiaojie Yuan
• Ye Yuan
• Yulin Yuan
• Yuanhao Yue
• Reza Zafarani (Syracuse University)
• Gabriela Zago
• Marcos Zampieri
• Savvas Zannettou
• Rowan Zellers
• Richard Zemel
• Zhi Zeng
• Luke Zettlemoyer
• Runzhe Zhan
• Beichen Zhang
• Huaiwen Zhang
• Ji Zhang
• Junjie Zhang
• Junyao Zhang
• Kunpeng Zhang
• Luoxi Zhang
• Ming Zhang
• Mingqing Zhang
• Shuning Zhang
• Tianhang Zhang
• Xiangyu Zhang
• Xiaoqing Zhang
• Xiaoying Zhang
• Xulang Zhang
• Yue Zhang
• Yulin Zhang
• Yunhao Zhang
• Zheng Zhang
• Zihan Zhang
• Peilin Zhao
• Sebastian Zhao
• Tianxiang Zhao
• Wayne Xin Zhao
• Xiangyu Zhao
• Zongyao Zhao
• Bing Zheng
• Vincent W. Zheng
• Fan Zhou
• James Zhou
• Jianlong Zhou (RMIT University)
• Kun Zhou
• Peng Zhou
• Qianli Zhou
• Wei Zhou
• Xinyi Zhou (Syracuse University)
• Zhoujun Li
• Chen Zhu
• Huaisheng Zhu
• Kaijie Zhu
• Yongchun Zhu
• Daniel M. Ziegler
• Jan Zilinsky
• Jonathan Zittrain
• Dimitrina Zlatkova
• Fabiana Zollo
• Andy Zou
• Arkaitz Zubiaga
• Reza Zubiaga

Topics

See all topics — research themes and methods.

• 2016 U.S. election interference
• 2016 Election Misinformation
• 2016 Garimella Quantifying Controversy
• 2016 Ma Rumor Rnn
• 2018 Ma Fakenewsnet
• 2018 Vosoughi Spread False Claims
• 2019 Raffel T5 Transfer Learning
• 2020 Ribeiro Radicalization Youtube
• 2022 Wang Detecting Propaganda
• 3D Reconstruction
• AI Governance
• AI Safety
• AI Security
• RLHF
• Semeval 2016 Task 6
• Abstractive Summarization
• Abusive language detection
• Accuracy
• Addiction
• Adversarial Attacks on Misinformation Detection
• Adversarial attacks on machine learning
• Adversarial Examples
• Adversarial learning for fake news detection
• Adversarial Machine Learning
• Adversarial Misinformation
• Adversarial Machine Learning
• Adversarial robustness
• Adversarial Robustness QA
• Adversarial testing
• Adversarial training
• Agent-Based Modeling
• Agent-Based Models
• AI Alignment
• AI-Assisted Claims
• AI-based Credibility Assessment
• AI-Generated Content
• AI Generated Content Detection
• AI Generated Misinformation
• AI-Generated Text Detection
• AI governance and compliance
• AI in Education
• AI Safety
• Algorithmic amplification
• Algorithmic Transparency
• Algorithms Platform Governance
• Alignment
• ALT Right
• Annotation bias
• Annotation Dataset
• Annotation Methodology
• Annotator Demographics
• Artificial Intelligence
• Astroturf
• Astroturfing
• Attention
• Attention mechanisms in NLP
• Attention Mechanisms NLP
• Audience analysis and measurement
• Author Profiling
• Authorship Analysis
• Automated Fact Checking
• Backfire effects and hostile media perception
• Bail Affective Polarization Echo Chambers
• Bail Exposure Opposing Views
• Baly Predicting Factuality
• Barabasi Albert 1999
• Barsela Cross Platform Misinformation
• Bayesian Inference
• Behavioral Dynamics
• Behavioral Economics
• Behavioral Intervention
• Behavioral Nudges
• Belief formation and epistemology
• Belief Functions
• Belief In Misinformation
• Belz Comparing Automatic
• Benchmark datasets and evaluations
• Benchmarking
• BERT and transformer embeddings
• BERT Embeddings
• Bias and Fairness
• Bias detection
• Bias in Language Models
• Bias In ML
• Bias in language models
• Biological Signals
• Biometrics
• Bot detection
• Bot Networks
• Botometer
• Brady Moral Emotion Diffusion
• Model calibration and confidence
• Cascade Analysis
• Cascade dynamics and prediction
• Cascade Models
• Cascade Prediction
• Castillo 2011 Credibility
• Causal Inference
• Center for an Informed Public
• Cheng 2014 Cascades Predicted
• Chinese Language Processing
• Cinelli Echo Chamber Social Media
• Citation Faithfulness
• Civic engagement
• Claim Credibility Assessment
• Claim Validation
• Claim Verification
• Claim check-worthiness detection
• Classification
• Clickbait
• Climate change communication and misinformation
• Cognitive Biases
• Cognitive reasoning
• Cognitive Reflection
• Collective Behavior
• Collins 2001 Convolution Kernels
• Community Analysis
• Community Detection
• Community notes
• Complex Contagion
• Compression Artifacts
• Computational Argumentation
• Computational fact checking
• Computational Journalism
• Computational Linguistics
• Computational modeling
• Computational Propaganda
• Computational social science and large-scale text analysis
• Concept Drift
• Confirmation Bias
• Conformity bias and social pressure
• Confounding
• Conover Political Networks
• Scientific consensus communication
• Conspiracy theories
• Misinformation containment
• Content Analysis
• Content authentication
• Content-based fake news detection
• Content-based features for misinformation detection
• Content moderation
• Context Aware Rumour Detection
• Continual Learning
• Continued influence effect
• Contrastive learning for detection
• Controversy Detection
• Conversation Analysis
• Coordinated inauthentic behavior
• Copyright Issues
• Corporate influence on scientific discourse and policy
• Correction effectiveness
• Counter-Narratives
• COVID-19 Misinformation and Infodemic
• COVID-19 misinformation and the infodemic
• Creator Intent
• Credibility assessment for fake news detection
• Crisis Informatics
• Cross-domain generalization
• Cross-domain learning for fake news
• Cross-lingual detection and transfer learning — training on one language, testing on others; transfer learning for low-resource languages.
• Cross-lingual misinformation
• Cross-Platform Analysis
• Crowd-sourced fact-checking
• Crowdsourcing
• Crowdsourcing Data Quality
• Cyber Bullying Detection
• Cyberbullying
• Cyberbullying Detection
• Cybersecurity
• Data augmentation
• Data literacy
• Data Mining
• Data Poisoning
• Data Privacy
• Data Protection
• Data quality
• Data Quality Hate Speech
• Data science
• Data Sharing
• Data-to-Text Generation
• Dataset curation
• Dataset development and curation
• Dataset quality
• Datasets and benchmarks
• Datasets and Benchmarks
• Davidson 2017 Hate Speech
• De Cristofaro Lobo 2018
• De-skilling and automation
• Debunking and correction interventions
• Deception Detection
• Deep learning
• Deep learning approaches
• Deepfake Detection
• Deepfakes
• Deepwalk
• Dehumanization
• Demographic Bias
• Dempster-Shafer Theory
• Dense Retrieval
• Fake content detection
• Detection difficulty of AI-generated content
• Detection Evasion
• Detection Focused Approaches
• Fake news detection methods
• Dialogue Generation
• Misinformation in diaspora and immigrant communities — misinformation targeting immigrant communities through messaging apps; platform moderation inequality; language-specific fact-checking gaps.
• Differential Privacy
• Diffusion models
• Diffusion Networks
• Digital ecosystems
• Digital Governance
• Digital literacy
• Digital media forensics
• Digital Policy
• Digital Rights
• Discourse Structure
• Disinformation
• Disinformation Campaigns
• Disinformation Detection
• Disinformation Generation
• Distributed Systems
• Divisive content
• Document Structure Learning
• Domain adaptation
• Dual-use technology
• Early detection of misinformation
• Eating Disorders and Pro-Eating Disorder Content
• Echo Chambers
• Echo Chambers Polarization
• Editorial autonomy
• Education
• Election coverage
• Election Integrity
• Election interference and information warfare
• Emergent Abilities
• Emergent events
• Emotion Recognition
• Emotional language in online discourse
• Ensemble methods
• Epidemic Models
• Epidemic Processes
• Epidemiology
• Ethics in language models
• Evaluation Methodology
• Evaluation metrics for language models
• Event Detection
• Evidence
• Evidence-based fake news detection
• Evidence Based Fact Verification
• Evidence Based Reasoning
• Evidence conflict in fact-checking
• Evidence Extraction
• Evidence Ranking
• Evidence Retrieval
• Evidence Synthesis
• Explainability in misinformation detection
• Explainable AI
• Explainable fake news detection
• Extremism and radicalization
• Face Forensics
• Face Forgery Detection
• Face manipulation
• Face Recognition
• Face Reenactment
• Face synthesis
• Face tampering detection
• Facebook and disinformation
• Facebook Oversight Board
• Facial expressions
• Facial manipulation detection
• Fact-checking and corrections
• Fact Verification
• Fact Verification And Checking
• Factual Consistency
• Factuality in large language models
• Factuality assessment
• Factuality In NLP
• Fairness
• Fairness in NLP
• Fairness NLP
• Faithfulness NLG
• Fake Account Detection
• Fake accounts
• Fake news
• Fake news audience and consumption
• Fake news detection datasets and benchmarks
• Fake news detection
• Fake news identification
• Fake news prevalence
• Fake News Propagation Simulation
• Fake News Sharing Behavior
• Fake Reviews
• False information
• Far-right and white nationalist content online
• Feature engineering for fake news detection
• Fever Dataset
• Feverous
• Few-Shot Learning
• Field experiments
• Filter Bubbles
• Fine-grained fake news classification
• Fine Tuning
• Foreign Interference
• Forensics
• Foundation Models
• Frame analysis
• Framing
• Framing bias
• Framing detection
• Fraud Detection
• Fundamental theories of fake news
• Game Theory
• Gab
• Gamification in media literacy and misinformation interventions
• GAN Detection
• GAN-Generated Video
• Gehrmann Challenges Detection
• Gehrmann Visually Grounded
• Generated text detection — detecting machine-generated versus human-written text using statistical and discriminative methods.
• Generative Adversarial Networks
• Generative AI
• Generative AI Misuse
• Generative Engine Optimization
• Generative Model Detection
• Generative Models
• Generative search poisoning
• GNN Training
• Graells Garrido Intermediary Topics
• Graph Based Detection
• Graph-Based Methods
• Graph Embedding
• Graph Neural Networks
• Graph Processing
• Graph Representation Learning
• Graph theory
• Group anomaly detection
• Group behavior detection
• Gru Rnn
• Guardrail evaluation
• Hallucination in language models
• Hallucination Detection
• Hallucination In Language Models
• Hallucination Mitigation
• Hallucinations in language models
• Harmful content detection
• Hate speech detection and moderation
• Hate speech detection
• Health misinformation
• Heterogeneous Graph Neural Networks
• Hierarchical attention mechanisms
• Hierarchical propagation networks
• Hoaxes
• Hoaxy
• Homophily
• Hostile rhetoric
• Hover
• Howcroft Twenty Years
• Human-AI Interaction
• Human Computer Interaction
• Human evaluation
• Humor Detection
• Hyperpartisan news detection
• Ideological bias
• Illusory-truth effect and fluency heuristics
• Image-based misinformation
• Image Caption Generation
• Image forensics
• Image manipulation
• Image splicing
• Image-text relationships
• Image Verification
• Implicit Bias
• In-Context Learning
• Influence Operations
• Influencers and their role in online discourse
• Infodemic
• Information Cascade
• Information Cascades
• Information Credibility
• Information Diets
• Information diffusion in social networks
• Information disorder
• Information ecosystems
• Information Extraction
• Information Fusion
• Information integrity
• Information literacy
• Information operations
• Information Retrieval
• Information retrieval attacks
• Information Retrieval Poisoning
• Information Spread
• Information Warfare
• Inoculation and prebunking
• Instruction Following
• Instruction Tuning
• Intent detection and classification
• Internet Culture Fringe Communities
• Internet Research Agency
• Interpretability
• Intersectionality
• Intersectionality Framework
• Intervention Strategies for Misinformation
• Inverse Scaling
• Irony Detection
• Jailbreaking
• Journalism and professional practice
• Journalism and AI
• Journalism Norms
• Junk News Detection
• Knowledge Augmentation
• Knowledge Base Completion
• Knowledge Editing
• Knowledge graphs
• Knowledge Grounding
• Knowledge-Intensive NLP
• Knowledge Representation In Neural Networks
• Knowledge Retrieval
• Knowledge Transfer
• Kwon 2017 Rumor Detection
• Label noise
• Label propagation
• Label quality
• Language Generation
• Language Model Alignment
• Language model truthfulness
• Language Models
• Large Language Models
• Latent Knowledge
• Learning With Noisy Labels
• Ledwich Zaitsev Algorithmic Amplification
• Liao Echo Chambers
• Liar Dataset
• Linguistic Analysis of Fake News
• Linguistic style detection
• Link Prediction
• Literacy interventions
• LLM Alignment
• LLM-based detection and analysis
• Large language model evaluation
• LLM-based fact-checking
• LLM-generated content
• LLM-Generated Misinformation
• LLM-generated text detection
• LLM Hallucination
• LLM-Generated Misinformation
• LLM Robustness
• LLM Safety and Adversarial Robustness
• LLM Safety Alignment
• LLM Watermarking
• Logical Fallacies
• Long-Context Language Models
• Low-Resource Language Processing
• LSTM Gating Signals
• Lukasik Stance Classification Hawkes
• Machine-generated text detection
• Machine learning
• Machine Learning Feature Engineering
• Machine Learning Security
• Machine Translation
• Mainstream media and misinformation
• Malicious intent in disinformation
• Manipulation Detection
• Mathematical Modeling
• Media Authentication
• Media bias
• Media bias detection
• Media characterization and landscape analysis
• Media consumption
• News ecosystems and institutional change
• Media Forensics
• Media literacy
• Media manipulation
• Media profiling
• Medical NLP
• Membership Inference
• Meme culture and politics
• Message design and persuasion
• Meta-analysis and systematic reviews
• Meta-learning
• Methodology
• Micro-video misinformation
• Mishkin Dall E 2 Misuse
• Misinformation
• Misinformation amplification
• Misinformation Attacks
• Misinformation Control
• Misinformation and fake news detection
• Misinformation Detection Methods
• Misinformation diffusion and spread
• Misinformation in retrieval-augmented generation
• Misinformation interventions
• Misinformation Interventions
• Misinformation mechanisms
• Misinformation Propagation
• Psychology of misinformation
• Misinformation interventions and responses
• Misinformation spread and diffusion
• Misinformation Susceptibility
• Misuse
• Model Alignment
• Model efficiency and compression
• Model Evaluation
• Model Interpretability
• Model Merging
• Model Reliability
• Model safety
• Model Security
• Moral judgment and ethics of sharing misinformation
• Moral psychology and moral discourse
• Morales Gianmarco
• Motivated reasoning
• Multi-Agent Systems
• Multi-class classification
• Multi-document summarization
• Multi Hop Reasoning
• Multi-objective optimization
• Multi Platform Analysis
• Multi Stakeholder Governance
• Multi-task learning
• Multilingual Analysis
• Multilingual fake news detection — parallel datasets and models across multiple languages.
• Multilingual Disinformation
• Multilingual Fact Checking
• Multilingual Misinformation
• Multilingual Models
• Multimedia Forensics
• Multimodal Detection
• Multimodal Analysis
• Multimodal fake news detection
• Multimodal fake news
• Multimodal fake news detection
• Multimodal Learning
• Multimodal Misinformation
• Multimodal Misinformation Detection
• Munger Phillips Recommendation Algorithm Bias
• Munson Presenting Diverse Political
• Myths
• Natural Language Generation
• Natural Language Inference
• Natural Language Processing
• Nela Gt 2021
• Nela2017
• Network analysis of misinformation
• Network Based Detection
• Network Based Misinformation Detection
• Network Dynamics
• Network Resilience
• Network science
• Networks
• Neural approaches to fake news detection
• Neural Language Model
• Neural language models
• Neural Machine Translation
• Neural Network Architectures
• Neural Network Transparency
• Neural networks
• Neural NLP
• Neural-symbolic AI
• Neural text generation
• News consumption patterns
• News credibility and source assessment
• News Feed Algorithms
• News Source Quality
• News values and editorial judgment
• NLP
• NLP Benchmarks
• NLP evaluation
• NLP Interpretability
• NLP methods
• NLP Robustness
• Non-Consensual Intimate Imagery
• Non English Content
• Numeracy and quantitative reasoning
• Offensive AI
• Offensive language detection
• Online activism
• Online Harassment
• Online Manipulation
• Online regulation
• Online subcultures
• Opinion Dynamics
• Opinion Manipulation
• Opinion Leaders Theory
• Ordinal regression
• Ordinal Regression Foundation
• Out-of-context misinformation detection
• Partisanship
• Personality Susceptibility
• Personality Traits and Fake News Susceptibility
• Personalized NLP
• Persuasion
• Phrase Embeddings
• Physiological Forensics
• Pizzagate Analysis
• Platform Design
• Platform algorithms and curation
• Platform Amplification
• Platform auditing and analysis
• Platform governance
• Platform moderation
• Platform Policy
• Platform Responsibility
• Platform Safety
• Polarization
• Polarization and Echo Chambers
• Political polarization and misinformation
• Policy
• Policy and regulation
• Political bias in fake news detection
• Political Bubbles
• Political communication
• Political discourse
• Political extremism and radicalization
• Political Misinformation
• Political polarization and ideological echo chambers
• Political security
• Politifact Dataset
• Polization Twitter
• Positional Bias
• Post Processing Neural Text
• Pre-trained language models
• Prebunking and inoculation against misinformation
• Prediction-Powered Inference
• Presentation Attacks
• Privacy in AI systems
• Private social media
• Proactive Detection
• Probabilistic Graphical Models
• Probability-based detection
• Prompt-based methods
• Prompt Engineering
• Prompt injection
• Prompt Optimization
• Prompting
• Propaganda
• Propaganda And Framing
• Propaganda detection
• Propaganda Research
• Propaganda techniques
• Propagation
• Propagation Analysis
• Propagation-based fake news detection
• Propagation Models
• Pseudo-labeling
• Psychological Drivers Misinformation
• Psychological Mechanisms
• Psychological Theory
• Psychology of belief formation and reasoning
• Digital public sphere and online deliberation
• Publication Accountability
• Publisher Bias
• QAnon
• Quality assessment
• Question Answering Systems
• Radicalization pathways and mechanisms
• RAG Attacks
• Ranking algorithms
• Real-Time Detection
• Reasoning Language Models
• Recommendation algorithms and content discovery
• Recommender systems
• Recurrent Neural Networks
• Recursive Neural Networks
• Red teaming
• Reddit
• Regression
• Regulation
• Reinforcement Learning
• Reinforcement Learning From Human Feedback
• Reinforcement Learning from Human Feedback
• Reinforcement Learning NLG
• Representation learning
• Responsible AI
• Retrieval-Augmented Generation
• Retrieval-Based Methods
• Rhetoric
• Rhetorical Structure Theory
• Risk communication
• RLHF Training
• Robust NLP Systems
• Robustness in language models
• Rubin 2015 Deception Detection
• Ruchansky Csi
• Rumor Amplification
• Rumor Cascade Models
• Rumor detection on social media
• Rumor Detection Temporal
• Rumor evolution and mutation
• Rumor propagation on social networks
• Rumor Verification
• Rumors
• Rumour detection
• Rumour Verification
• Rumour Verification Surveys
• Russian disinformation and state-sponsored information operations
• Safety
• Sarcasm Detection
• Satire detection
• Scalability
• Scale-free networks
• Scaling laws in language models
• Schramowski Stable Diffusion Filter
• Science Misinformation
• Search engine manipulation
• Security in AI systems
• Selection Bias
• Selective Exposure
• Semantic Analysis
• Semantic Parsing
• Semantic Preservation
• Semantic search poisoning
• Semantic similarity and relatedness
• Semi Supervised Learning
• Sensationalism
• Sentiment Analysis
• Sequence Modeling
• Sequence To Sequence Models
• Shared tasks and benchmarks
• Shu Hierarchical Propagation
• Shu Social Context Trifn
• Small-world networks
• Snopes Dataset
• Social Bias
• Social Bot Detection
• Social bots
• Social-context-based fake news detection
• Social Dimension Construction
• Social Influence
• Social learning and belief transmission
• Social media and misinformation
• Social Media Amplification
• Social Media Analysis
• Social Media Behavior
• Social media discourse
• Social media engagement
• Social Media Harms
• Social media manipulation
• Social media polarization and echo chambers
• Social media fact-checking and verification
• Social movements
• Social Network Security
• Social networks and online communities
• Social spambots
• Social tie formation
• Source reliability
• Spatiotemporal information for misinformation detection
• Spectacle
• Spectacle Theory
• Spectral Analysis
• Spiral of Silence
• Srivastava Beyond Imitation
• Stance classification
• Stance Detection
• State Sponsored Disinformation
• State-sponsored information operations
• Statistical Features
• Steganalysis
• Strategic communication
• Strategic Information Operations
• Student Learning
• Stylometry
• Substance Abuse and Drug Misinformation
• Summarization
• Survey and review papers
• Synthesis-Stage Attacks on LLMs
• Synthetic Content
• Synthetic media
• Synthetic Media Detection
• Synthetic Media Generation
• Synthetic Text Generation
• System dynamics
• Taxonomy and typology
• Teaching
• Technology Governance
• Temporal Analysis
• Temporal patterns
• Temporal Prediction
• Temporal Rumour Evolution
• Terminology and conceptual frameworks
• Text classification
• Text embeddings and word representations
• Text generation
• Text Only Fake News Approaches
• Text Representations
• Threat modeling
• Topic modeling
• Toxicity detection
• Transfer learning for fake news detection
• Transformer Architecture
• Transformer Based Detection
• Transformers
• Transnational Governance
• Transparency in technology and media
• Transparency in Journalism
• Tree structured neural networks
• Trust in institutions and communicators
• Trust in science and scientists
• Trustworthiness of language models
• Trustworthiness Language Models
• Trustworthy AI
• Truthfulness
• Twitter
• Twitter Analysis
• Twitter Dataset
• Twitter Datasets
• Twitter Security
• Typology
• Uncertainty quantification
• Uncertainty estimation
• Uncertainty Quantification
• User Attributes
• User behavior
• User Behavior Analysis
• User comments for fake news detection
• User Context
• User engagement patterns and fake news
• User interactions and engagement patterns
• User preferences
• User profiles for fake news detection
• User Propagation Based Detection
• User Susceptibility to Fake News
• User Susceptibility Characterization
• Vaccine hesitancy and vaccine safety concerns
• Van Der Lee Best Practices
• Variational autoencoders
• Variational Inference
• Veracity Assessment
• Veracity prediction
• Verification
• Video classification and content analysis
• Video Forensics
• Video Misinformation
• Viral Spread
• Vision-language models
• Vision transformers
• Visual misinformation
• Visual-semantic inconsistency
• Wang Eann Multimodal
• Watermarking
• Watts Strogatz 1998
• Weak supervision
• Wechat
• Wei Scaling Laws Adversarial
• WhatsApp and Misinformation
• Wisdom of crowds
• Word Embeddings
• YouTube and political content
• YouTube and political radicalization
• Zero-shot detection
• Zero-shot learning
• Zombie Rumors

Datasets

See all datasets.

• NELA-GT-2022 — 1.78M news articles from 361 sources (2022); source-level MBFC labels (factuality 0–5, conspiracy/pseudoscience); 346K embedded tweets; fifth NELA release with stabilized collection; SQLite and JSON formats.
• NELA-GT-2018 — 713K news articles from 194 sources (2018); engagement-independent collection; source-level labels from 8 assessment sites (NewsGuard, Pew Research, Wikipedia, OpenSources, MBFC, AllSides, BuzzFeed, PolitiFact); multi-dimensional ground truth.
• NELA-GT-2019 — 1.12M news articles from 260 sources (2019); source-level labels from 7 assessment sites (MBFC, AllSides, PolitiFact, etc.); 3-class aggregated reliability label; SQLite and JSON formats.
• NELA-GT-2020 — 1.78M news articles from 519 sources (2020); source-level MBFC labels; novel embedded tweets feature (410K tweets); covers COVID-19 and 2020 U.S. election; SQLite and JSON formats.
• MM-COVID — 3,981 fake news pieces in six languages (English, Spanish, Portuguese, Hindi, French, Italian) with 7,192 tweets; multilingual and multimodal COVID-19 dataset enabling cross-lingual detection research.
• FakeNewsNet — PolitiFact + GossipCop; news content + Twitter social context; labels from professional fact-checkers.
• ReCOVery — 2,029 COVID-19 news articles from 60 publishers; publisher-level NewsGuard/MBFC credibility labels; 140,820 tweets; multimodal (text, image, social).
• CHECKED — 2,104 Weibo microblogs with per-item expert labels (344 fake, 1,760 real); Chinese COVID-19; includes images, video, and full propagation threads.
• Weibo21 — 9,128 Weibo microblogs (4,488 fake, 4,640 real) across 9 domains (Science, Military, Education, Disasters, Politics, Health, Finance, Entertainment, Society); first multi-domain fake news dataset from a single platform; addresses domain shift for cross-domain detection.
• Fake And Real News — 10,558 English news articles (binary fake/real); fake articles from 2016 Kaggle election dataset; real articles from AllSides/major outlets; 50% null accuracy.

• FUBU-EPSTEIN — 54.38M Twitter statuses (Aug 2019–Apr 2023) on the Jeffrey Epstein case; annotated with conspiracy, misinformation, sentiment, toxicity, and moral emotion; 37M-edge social contact graph; public anonymized release on Zenodo.

• CMU-MisCOV19

• CoAID
• COVID-19 Twitter Chatter Dataset
• DFDC: DeepFake Detection Challenge Dataset
• Fake News Corpus
• Fakeddit
• Media Bias/Fact Check (MBFC)
• MultiFC
• OLID
• Pheme Project
• SwapMe and FaceSwap Dataset
• VidTIMIT Deepfake Dataset
• VLDBench
• Wang Deepfake Detection Dataset
• WildFakeBench

Tools & libraries

See all tools (populated by ingest workflow)

Videos & talks

See all videos (populated by ingest workflow)

• Academic Review: Political Deepfakes — Farid on deepfake detection in the 2024 election, forensic techniques (perspective geometry, shadow physics, mannerisms), real-world examples, and upstream policy interventions.
• Misinformation, Content Moderation, and Information Integrity — Wardle on the trilemma of platform governance (authority, scale, fragmentation); solutions via crowdsourced assessment, research infrastructure, and coordinated networks.
• The Psychology of Misinformation and Psychological Inoculation — misinformation susceptibility measurement via the MIST test; psychological mechanisms of belief change; why debunking fails; inoculation theory and the Bad News game as preventive interventions.
• Misinformation, Disinformation, and Psychological Inoculation — psychological inoculation theory and evidence from the Bad News game; pre-bunking as scalable alternative to fact-checking; application to Ukraine-Russia conflict; robustness across demographics and over time.
• False News Spreads Faster and Farther Than Truth Online — talk on differential spread of true vs. false news on Twitter; human rather than bot drivers; novelty bias mechanism.
• Information and Misinformation Spread on Social Networks — network science of information diffusion; power-law virality patterns; echo chambers as structural inevitability; social bots and computational propaganda; role of limited attention and platform architecture.
• How Polarization May Help Combat Misinformation — Rand on scaling fact-checking via community-sourced systems (like Twitter's Community Notes); theoretical model of competing motivations (accuracy vs. partisanship); empirical evidence that polarized individuals effectively flag false information.
• Belief, Evidence, and Elites in the Age of Mass Information — O'Connor on false belief formation as a social phenomenon; historical cases (variolation, tobacco industry); conformity bias and polarization in tight communities; industry influence via selective evidence curation; journalism's fairness bias undermining accuracy; social media amplification.
• Deepfakes, Detection, and the Misinformation Landscape — Farid on synthetic media detection via physics-based forensics (perspective, shadows, mannerisms); weaponization case studies (financial fraud, geopolitical deception, infiltration); regulatory and authentication solutions (C2PA, supply-chain intervention); critique of reactive detection and need for multi-stakeholder responsibility.
• Misinformation and Data Literacy — data literacy as a defense against misinformation; chart manipulation in mainstream media; educational interventions (Calling Bullshit); emerging synthetic-media threats.
• Misinformation, Disinformation, and Crisis Informatics — how misinformation propagates during crises (COVID-19, elections); case studies of conspiracy theories (5G causes COVID, Bill Gates engineered the virus); real-world harms (protests, graffiti); mixed methods for studying information diffusion.

• Misinformation in Diaspora Communities — messaging-app misinformation in immigrant communities; platform moderation gaps; language-specific fact-checking inequality.

• Accuracy, Reasoning, and Misinformation (2026)

• Bad News: Building Resilience to Misinformation Through Inoculation (2026)
• Challenges with Large Language Models: Implications for Fake News, Fact-Checking, and Misinformation Detection (2026)
• Disinformation and Platform Governance: Moderation, Alt-Tech, and Radicalization (2021)
• Misinformation, Propaganda, and the Era of Synthetic Media (2018)
• Social Bots and Misinformation: Detection, Diffusion, and Mitigation (2017)

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