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Fact-checking & verificationPlatforms, policy & society When Imperfect Automated Fact-Checking Backfires: Social Learning in the Presence of MisinformationAutomated fact-checking tools are free and fast but error-prone; manual checking is costly but accurate. This paper studies whether adding a free automated tool alongside manual fact-checking improves social welfare (defined as the probability a cascade reaches the correct conclusion). Liu & Cao · arXiv · 2026

Fact-checking & verificationPlatforms, policy & society Fact-check? True!: Tracking Verification Requests and the Absence of Truth Verdicts across Correction SystemsExamining 47,899 X/Twitter users asking Grok (X's AI assistant) whether posts are true, the authors find that ~51% of questioned posts are actually accurate, yet correction systems (Community Notes, professional fact-checkers) focus overwhelmingly on false claims. Professional fact-checkers publish true verdicts in only 8–11% of their… Wack & Warren · arXiv · 2026

Platforms, policy & societyDatasets & evaluation When Platforms Pay Users to Correct Misinformation: Revenue Incentives and Governance Capacity in Weibo AnnotationsWeibo Annotations is a crowdsourced governance system that lets users add corrections and contextual information to disputed claims. In May 2025, Weibo introduced monetary rewards for approved annotations. Lin et al. · arXiv · 2026

Psychology & interventionsFact-checking & verification A misinformation playbook sharpens judgement without breeding cynicismThis paper analyzes 214,646 fact-checked claims across 51 languages to map misinformation's persuasion playbook—48 recurring strategies organized under social influence, information manipulation, and emotional appeal. Rather than flagging specific false claims, teaching these meta-strategies improves how both AI models and humans judge… Guo et al. · arXiv · 2026

LLMs & generative AIFact-checking & verification VEX-Bench: Benchmarking Verification Complexity of LLM-Generated MisinformationThis paper introduces VEX-Bench, a unified benchmark for evaluating how difficult it is to verify LLM-generated misinformation. The benchmark measures verification complexity across five dimensions: checkability (whether claims can be checked), harm significance, source credibility signals, impostor legitimacy, and verification cost. Huang et al. · NeurIPS 2026, Track on Evaluations and Datasets · 2026

LLMs & generative AIPsychology & interventions AI-Enabled Human Memory Manipulation: Misleading AI-Generated Summaries Distort Human MemoryLarge language models frequently generate erroneous summaries of videos, including critical omissions and hallucinated details. In a human-subjects experiment, participants who read AI-generated summaries containing misleading information were significantly less likely to accurately recall the original event—demonstrating that AI… Sim et al. · arXiv · 2026

Fact-checking & verificationLLMs & generative AI To Trust or Not to Trust: Retrieval-Augmented Fact Checking in SpeechLarge Audio Language Models (LALMs) exhibit a significant text-speech modality gap: they verify written claims reliably but fail on spoken ones, even from the same model. The paper introduces VeriSpeak, a benchmark of 3,879 spoken claims with temporal, geographical, and relational facts, and demonstrates that retrieval-augmented LALMs… Mazumder et al. · arXiv · 2026

Fact-checking & verificationLLMs & generative AI SoK: Formal Methods for Fact-Checking and Information IntegrityThis systemization of knowledge surveys formal methods (logic, model checking, theorem proving, symbolic reasoning) applied to fact-checking systems. The authors organize 121 works by assurance level and identify two critical gaps: constructing checkable claims and correcting errors in published verdicts. Kekatos et al. · arXiv · 2026

LLMs & generative AIPlatforms, policy & society Making AI-Assisted Claims Independently Challengeable: Publication Authority and a Protocol for Falsifiable Publication RecordsAI-assisted claims can appear authoritative even when evidence, analysis, and authorization refer to different states—creating a coordination problem. This work introduces Publication Authority (PA) as a non-transferable, single-use capability governing publication transitions, and proposes PAC-2026 SF-4, an executable specification… Tiltack et al. · arXiv · 2026

LLMs & generative AIPlatforms, policy & society A Multi-Stage Agentic Framework for Effective Counter-Narrative Generation and RefinementCounter-narratives are a scalable defense against misinformation and hate speech, but manual authoring is slow and labor-intensive. This paper proposes a multi-stage agent-based framework using LLMs to generate and iteratively refine counter-narratives on pro-Russian misinformation about Ukraine. Kronfeld et al. · arXiv · 2026

Fact-checking & verificationDatasets & evaluation CoVer: Conflict-Aware Claim VerificationSocial media fact-checking frequently encounters conflicting evidence where different sources support or refute a claim. This paper proposes CoVer, a framework that explicitly resolves evidence-level conflicts (contradictory stances from different sources) and aggregation-level conflicts (sub-claims within complex claims yielding… Zhang et al. · arXiv · 2026

LLMs & generative AIFact-checking & verification Calibrating Small Language Models for Claim Check-Worthiness DetectionClaim check-worthiness detection is computationally expensive when using large language models, but essential for scaling fact-checking pipelines. This paper proposes NN-PPI (Nearest Neighbor Prediction-Powered Inference), a post-hoc calibration technique that improves small LM predictions using a small labeled calibration set and… Amatya et al. · arXiv · 2026

LLMs & generative AIPlatforms, policy & society The GenAI Catch-22: Use of Generative Artificial Intelligence in Norwegian Newsrooms During the 2025 Parliamentary ElectionA longitudinal qualitative study of four major Norwegian newsrooms (NRK, TV2, VG, Dagbladet) during the 2025 parliamentary election reveals a "GenAI Catch-22": newsrooms need human expertise to oversee generative AI systems and prevent errors, but extensive use of GenAI leads to de-skilling that erodes that very expertise, rendering… Reisjå & Løvtie · arXiv · 2026

Detection methodsDeepfakes & multimodal What's Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News PreviewsNews previews (headline + image) on social media can mislead readers through selective omission of critical context present in the full article. This paper introduces MM-MISLEADING, a benchmark of 6,000 multimodal news previews annotated for misleading omissions, and proposes OMGUARD, a framework combining interpretation-aware… Li et al. · arXiv · 2026

Datasets & evaluationDetection methods A Guide to Misinformation Detection Data and EvaluationThis work surveys the largest collection of misinformation detection datasets to date (75 datasets from prior literature) and evaluates dataset quality across multiple dimensions. The authors assess whether datasets enable reliable veracity evaluation by analyzing keyword correlations, temporal correlations, and feasibility of claim… Thibault et al. · KDD '25 — 31st ACM SIGKDD Conference on… · 2025 LLMs & generative AIPlatforms, policy & society Controlled Change(Stub page — referenced in context of GenAI adoption in journalism. To be expanded with full paper details.) Dodds · 2024

LLMs & generative AIDetection methods DELL: Generating Reactions and Explanations for LLM-Based Misinformation DetectionLarge language models struggle with factuality and hallucinations in misinformation detection. DELL proposes a three-stage pipeline where LLMs generate synthetic user reactions from diverse perspectives, create explanations for proxy tasks (sentiment, framing, propaganda tactics), and merge predictions from task-specific experts. Wan et al. · arXiv · 2024

LLMs & generative AIDatasets & evaluation How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent AdvancesThis survey reviews methods for aligning large language models with ever-changing world knowledge without retraining from scratch. The authors systematically categorize approaches as either implicit (modifying LLM parameters directly via knowledge editing or continual learning) or explicit (augmenting LLMs with external resources via… Zhang et al. · arXiv · 2023

LLMs & generative AIFact-checking & verification Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-SpecificityThis comprehensive survey addresses the critical problem of factuality in large language models, examining how LLMs store and retrieve knowledge, analyzing the mechanisms through which they generate false or misleading content, and reviewing evaluation metrics and enhancement strategies. The paper distinguishes factuality issues from… Wang et al. · arXiv · 2023

Fact-checking & verificationLLMs & generative AI The Perils & Promises of Fact-checking with Large Language ModelsThis paper evaluates GPT-3.5 and GPT-4's ability to fact-check claims across English-only and multilingual datasets, showing GPT-4 substantially outperforms GPT-3.5 but both models show inconsistent accuracy when dealing with ambiguous verdicts. Critically, incorporating contextual information via Google Search significantly improves… Quelle & Bovet · arXiv · 2023

LLMs & generative AIFact-checking & verification Atlas: Few-shot Learning with Retrieval Augmented Language ModelsATLAS is a retrieval-augmented language model that achieves strong few-shot performance on knowledge-intensive tasks by jointly training a dense retriever with a sequence-to-sequence language model. Unlike standard language models that must memorize knowledge, ATLAS retrieves relevant documents at both pre-training and fine-tuning… Izacard et al. · arXiv · 2022

LLMs & generative AIDetection methods Discovering Latent Knowledge in Language Models Without SupervisionLanguage models trained with standard objectives (imitation, reward-based) can output false statements even when they internally "know" the truth. This paper proposes Contrast-Consistent Search (CCS), an unsupervised method that exploits truth's logical structure—a statement and its negation cannot both be true—to recover latent… Burns et al. · arXiv · 2022

Datasets & evaluationDetection methods NELA-GT-2022: A Large Multi-Labelled News Dataset for The Study of Misinformation in News ArticlesPresents the fifth NELA-GT dataset release: 1.78 million news articles from 361 outlets spanning all of 2022, with source-level veracity labels from Media Bias/Fact Check and embedded tweet data. Focuses on stabilizing collection infrastructure to achieve near-complete temporal coverage, enabling robust benchmarking of fake news… Gruppi et al. · arXiv · 2022

Psychology & interventionsFact-checking & verification The psychological drivers of misinformation belief and its resistance to correctionThis comprehensive review establishes that misinformation's power lies not in exposure but in how cognitive, social, and affective factors shape belief formation and persist even after correction. False beliefs arise from intuitive thinking, memory failures, emotional content, in-group bias, and source credibility judgments; corrections… Ecker et al. · Nature Reviews Psychology · 2022