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Fact-checking & verificationPsychology & interventions Fact-Checking: A Meta-Analysis of What Works and for WhomMeta-analysis of 30 studies on fact-checking effectiveness finds modest but significant positive effects (d = 0.29) on belief correction. However, effects are substantially moderated by motivated reasoning, political ideology, message design, and context: pro-attitudinal fact-checking works better than counter-attitudinal corrections,… Walter et al. · Political Communication · 2020

Platforms, policy & societySpread & networks Social media and vaccine hesitancyLarge-scale cross-national analysis finds that social media use to organize offline action predicts public vaccine safety skepticism, while foreign disinformation campaigns significantly reduce vaccination coverage year-over-year. A one-point increase on the five-point disinformation scale correlates with a two-percentage-point drop in… Wilson & Wiysonge · BMJ Global Health · 2020

Datasets & evaluationDetection methods CHECKED: Chinese COVID-19 Fake News DatasetCHECKED is the first Chinese-language, fact-checked COVID-19 social media dataset — 2,104 Weibo microblogs (344 fake, 1,760 real) spanning December 2019 to August 2020, labeled using Weibo's expert-operated Community Management Center for fake posts and People's Daily official account for real posts. Each microblog carries rich… Yang et al. · arXiv · 2020

Detection methodsLLMs & generative AI Fake News Early Detection: An Interdisciplinary StudyThis paper proposes a theory-driven, content-only model for fake news detection at publication time — before social-media propagation data exists. News articles are represented at four linguistic levels (lexicon, syntax, semantic, and discourse) using features grounded in social and forensic psychology; classical supervised classifiers… Zhou et al. · ACM · 2020

Spread & networksDetection methods A Survey of Fake News: Fundamental Theories, Detection Methods, and OpportunitiesThis comprehensive survey defines fake news across both broad and narrow framings, systematizes detection methods across four perspectives (knowledge, style, propagation, source), connects detection strategies to interdisciplinary theories from psychology and social sciences, and identifies six open research challenges including early… Zhou & Zafarani · ACM Computing Surveys · 2020

Datasets & evaluationDeepfakes & multimodal ReCOVery: A Multimodal Repository for COVID-19 News Credibility ResearchThe COVID-19 pandemic triggered a parallel "infodemic" of low-credibility news; this paper constructs ReCOVery, a multimodal repository of 2,029 COVID-19 news articles with binary credibility labels inherited from their publisher's NewsGuard/MBFC ratings, paired with 140,820 tweets tracking social spread. The key design trade-off is… Zhou et al. · CIKM '20 · 2020

Deepfakes & multimodalDetection methods SAFE: Similarity-Aware Multi-Modal Fake News DetectionFake news articles frequently pair misleading textual claims with irrelevant or manipulative images, creating a detectable gap between the two modalities. SAFE addresses this by extracting separate neural representations of a news article's text and images — using the same Text-CNN architecture applied to an image2sentence embedding for… Zhou et al. · arXiv · 2020

Spread & networksDetection methods Fake news propagation and detection: A sequential modelFoundational sequential social learning model of misinformation spread and fact-checking. Introduces the framework for analyzing how agents' decisions cascade through a network and how fact-checking affects collective outcomes. Papanastasiou · Management Science · 2020

Spread & networksPlatforms, policy & society Influence of fake news in Twitter during the 2016 US presidential electionUsing 171 million tweets from the five months preceding the 2016 U.S. election, this study found that 25% of news-sharing tweets contained fake or extremely biased content. Network analysis revealed distinct clusters of fake, biased, and mainstream news spreaders. Bovet & Makse · Nature Communications · 2019

Deepfakes & multimodalDetection methods Deep Learning for Deepfakes Creation and Detection: A SurveyComprehensive survey of deep learning methods for creating and detecting deepfakes, covering face-swapping and lip-syncing generation techniques (GANs, autoencoders, encoder-decoder pairs), detection approaches (visual artifacts, temporal features, forensics), and challenges in cross-dataset generalization. The paper discusses… Nguyen et al. · arXiv · 2019

Deepfakes & multimodalDetection methods The Emergence of Deepfake Technology: A ReviewThis review analyzes 84 news articles about deepfakes to synthesize understanding of synthetic video technology, its applications, threats, and countermeasures. Deepfakes—hyper-realistic videos created using AI (primarily GANs)—lower the barrier to video forgery and represent a significant threat to trust, elections, and security. Westerlund · Technology Innovation Management Review · 2019

Platforms, policy & societyBots & coordinated campaigns Facebook's role in Brexit — and the threat to democracyInvestigative journalist Carole Cadwalladr exposes the systematic abuse of Facebook as an instrument for election interference during the 2016 Brexit referendum. She documents illegal campaign spending, microtargeted disinformation, Cambridge Analytica's harvesting of 87 million user profiles, and traces connections between Brexit and… Cadwalladr · 2019

Deepfakes & multimodalPlatforms, policy & society How deepfakes undermine truth and threaten democracyDeepfakes use machine-learning technology to create fabricated audio and video of people doing or saying things they never did. Law professor Danielle Citron explores the dual threats: individual harm (non-consensual sexual deepfakes targeting women, especially minorities and journalists) and societal damage (eroding trust in… Citron · 2019

Platforms, policy & societyPsychology & interventions Inside the bizarre world of internet trolls and propagandistsJournalist Andrew Marantz spent three years investigating propagandists and online extremists, tracing how fringe conspiracy theories and hate speech go viral on social media platforms. He exposes how social media algorithms optimized for engagement enable propagandists to leverage emotion over truth, and documents the pipeline through… Marantz · 2019

Detection methodsSpread & networks Fake News in the 2016 and 2018 Campaigns: Prevalence, Detection, and EffectsNyhan presents empirical findings on fake news consumption in the 2016 and 2018 U.S. campaigns, drawing from survey data with browser tracking to measure actual article visits. While mainstream media reported on fake news as a widespread crisis, his research shows that only ~25% of Americans visited fake news websites, and consumption… Nyhan · Ohio State Center for Ethics and Human Values · 2019

Spread & networksBots & coordinated campaigns 5 Reasons Why Social Networks Make Us Vulnerable to MisinformationMenczer presents five fundamental factors that make social networks inherently vulnerable to misinformation spread. Information and misinformation propagate through networks at equivalent rates due to structural properties of scale-free networks combined with human cognitive limitations. Menczer · Aula Rostagni, University of Padova · 2019

Platforms, policy & societySpread & networks How You Can Help Transform the Internet into a Place of TrustWardle examines the systemic challenges of moderating harmful content at internet scale while preserving freedom of expression. She argues that the core problem is not technical (AI could theoretically detect violations) but structural: platforms and governments lack clear authority to decide what constitutes harmful speech, face… Wardle · 2019

Platforms, policy & society The platform governance triangle: conceptualising the informal regulation of online contentThis paper applies Abbott and Snidal's "governance triangle" framework to analyze how state actors, technology firms, and civil society organizations informally regulate online content through voluntary standards, codes of conduct, and multistakeholder initiatives. The analysis maps the European platform governance landscape, showing how… Gorwa · Internet Policy Review · 2019

LLMs & generative AIDetection methods RoBERTa: A Robustly Optimized BERT Pretraining ApproachA replication and improvement study of BERT pretraining showing that BERT was significantly undertrained. RoBERTa introduces four key improvements—training longer with larger batches and more data, removing the next-sentence-prediction objective, training on longer sequences, and dynamically masking tokens—to achieve state-of-the-art… Liu et al. · arXiv · 2019

Fact-checking & verificationDatasets & evaluation MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of ClaimsMultiFC is the largest publicly available real-world dataset of naturally occurring factual claims (34,918 claims) collected from 26 fact-checking websites, with rich metadata, evidence pages retrieved via Google Search, and entity linking to Wikipedia. The paper demonstrates that evidence ranking combined with veracity prediction… Augenstein et al. · arXiv · 2019

Fact-checking & verificationDatasets & evaluation A Richly Annotated Corpus for Different Tasks in Automated Fact-CheckingThe paper introduces SNOPES, a large-scale fact-checking corpus with 6,422 validated claims and 14,296 documents covering multiple domains (news, social media, blogs). Each claim is annotated with evidence text snippets, evidence stance (support/refute/no stance), and verdicts, enabling training of models for four core fact-checking… Hanselowski et al. · arXiv · 2019

Spread & networksPlatforms, policy & society Auditing Radicalization Pathways on YouTubeFirst large-scale quantitative audit of user radicalization on YouTube. The authors tracked 349 channels across three ideological communities (Intellectual Dark Web, Alt-lite, Alt-right) and found strong evidence that users systematically migrate toward more extreme content. Horta Ribeiro et al. · ACM 2020 Conference on Fairness,… · 2019

Detection methodsLLMs & generative AI Learning Hierarchical Discourse-level Structure for Fake News DetectionProposes HDSF, a framework that learns hierarchical discourse-level structure (dependency trees of sentences) for fake news detection without annotated corpora. Identifies three structure-related properties that distinguish fake from real news and shows HDSF achieves 82.19% accuracy, outperforming linguistic baselines. Karimi & Tang · arXiv · 2019

Datasets & evaluationDetection methods Predicting the Type and Target of Offensive Posts in Social MediaThis paper introduces OLID (Offensive Language Identification Dataset), a large-scale dataset of 14,100 English tweets annotated with a three-level hierarchical schema for detecting offensive content. The hierarchy distinguishes offensive from non-offensive posts (Level A), categorizes offense type as targeted insults or untargeted… Zampieri et al. · NAACL · 2019