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Spread & networksBots & coordinated campaigns On the Detection of Disinformation Campaign Activity with Network AnalysisState-sponsored disinformation campaigns on Twitter exhibit distinct coordination patterns (shared messages, hashtags, URLs, retweet cascades) that differ from legitimate coordinated activity. The paper proposes using coordination network features—extracted from daily account interactions—to train a binary classifier that detects… Vargas et al. · Cloud Computing Security Workshop · 2020

Spread & networksPlatforms, policy & society A first look at COVID-19 information and misinformation sharing on TwitterEarly-pandemic analysis of 21 million COVID-19 tweets showing Twitter conversations predict case increases by 2–5 days; identifies five prevalent myths (origin, flu comparison, heat, home remedies, vaccine) accounting for ~0.6% of tweets; finds most shared URLs point to reputable sources (CDC, WHO) rather than misinformation sites,… Singh et al. · arXiv · 2020

Bots & coordinated campaignsSpread & networks Uncovering Coordinated Networks on Social Media: Methods and Case StudiesThis paper proposes a general, unsupervised, network-based methodology to detect coordinated inauthentic behavior on social media. The approach identifies behavioral traces (handle changes, image sharing, hashtag sequences, co-retweets, synchronized actions) and constructs bipartite networks to discover clusters of accounts likely… Pacheco et al. · ICWSM · 2020

Spread & networksPlatforms, policy & society Exposure to untrustworthy websites in the 2016 US electionUsing combined survey and web-traffic data from the 2016 US presidential election, Guess et al. found that while 44.3% of Americans aged 18+ visited an untrustworthy website during the campaign, actual exposure was highly concentrated among Trump supporters (56.7% of Trump supporters vs. 27.7% of Clinton supporters). Facebook played a… Guess et al. · Nature Human Behaviour · 2020

Spread & networksPlatforms, policy & society Assessing the risks of 'infodemics' in response to COVID-19 epidemicsAnalysis of 112+ million geolocated Twitter messages about COVID-19 (January–March 2020) across 64 languages and countries. Develops an infodemic risk index (IRI) measuring exposure to unreliable news; demonstrates that waves of unreliable information precede pandemic waves, providing early-warning signals for policy intervention. Gallotti et al. · Nature Human Behaviour · 2020

Fact-checking & verificationPlatforms, policy & society Types, Sources, and Claims of COVID-19 MisinformationSystematic analysis of 225 fact-checked COVID-19 misinformation pieces reveals that most false content reconfigures existing information (59%) rather than fabricating entirely new claims. Public authorities are cited in 39% of misinformation, yet top-down claims from politicians and celebrities account for only 20% of volume but drive… Brennen et al. · Reuters Institute for the Study of… · 2020

Datasets & evaluationSpread & networks A large-scale COVID-19 Twitter chatter dataset for open scientific research - an international collaborationBanda et al. release a massive curated corpus of 800+ million COVID-19–related tweets collected from January through November 2020, with deduplication and language diversity. The dataset adheres to FAIR principles and provides both raw tweets and metadata (terms, bigrams, trigrams, hashtags, emojis, mentions) to support research on… Banda et al. · arXiv · 2020

Detection methodsSpread & networks Mining Disinformation and Fake News: Concepts, Methods, and Recent AdvancementsThis comprehensive tutorial surveys the landscape of fake news and disinformation research across three dimensions: understanding user engagement in information disorder, techniques for detecting and mitigating false content, and emerging challenges like fake news literacy and neural generation. The authors emphasize weak social… Shu et al. · Book chapter · 2020

Detection methodsSpread & networks A Survey on Computational Propaganda DetectionThis survey comprehensively reviews computational propaganda detection from two complementary perspectives: text analysis (NLP) and network analysis. The authors argue that propaganda differs from misinformation/disinformation by focusing on persuasion techniques (emotional appeals, logical fallacies) rather than falsity. San Martino et al. · arXiv · 2020

Datasets & evaluationSpread & networks Characterizing COVID-19 Misinformation Communities Using a Novel Twitter DatasetThis paper presents CMU-MisCOV19, a manually annotated Twitter dataset of 4,573 COVID-19 tweets across 17 categories, and characterizes misinformed versus informed online communities. Key findings: misinformed communities are significantly denser (higher network density), contain more bots (19% vs 11%), and demonstrate higher… Memon & Carley · arXiv · 2020

Datasets & evaluationSpread & networks CoAID: COVID-19 Healthcare Misinformation DatasetCoAID is a benchmark dataset for COVID-19 healthcare misinformation detection comprising 4,251 news articles (204 fake, 3,565 true), 28 fake claims and 454 true claims, 296,000 user engagements from social platforms, and 926 social media posts with ground-truth labels. The dataset bridges text content, user engagement features, and… Cui & Lee · arXiv · 2020

Spread & networksPlatforms, policy & society The COVID-19 Social Media InfodemicComparative analysis of COVID-19 information diffusion across five social media platforms (Twitter, Instagram, YouTube, Reddit, Gab) using epidemic models. All platforms exhibit infodemic characteristics (basic reproduction number R₀ 1), with platform-dependent differences in how misinformation spreads relative to reliable information. Cinelli et al. · arXiv · 2020

Spread & networksDetection methods Rumor Detection on Social Media with Bi-Directional Graph Convolutional NetworksProposes Bi-GCN, a graph convolutional network approach for rumor detection that models both top-down propagation patterns (how rumors spread from source) and bottom-up dispersion patterns (how rumors diffuse within communities). By capturing both directional characteristics jointly and incorporating source post features at each layer,… Bian et al. · AAAI 2020 · 2020

Spread & networksDetection methods What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media ContextThis paper predicts the political bias (left/center/right) and factuality (low/mixed/high) of news outlets by analyzing what articles they publish, who follows them on social media, and what Wikipedia says about them. The system achieves 85% accuracy on bias prediction by combining article text, YouTube video features, Twitter audience… Baly et al. · 58th Annual Meeting of the Association for… · 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

Spread & networksPlatforms, policy & society Causes and consequences of mainstream media dissemination of fake news: literature review and synthesisWhile fake news websites have limited direct reach, mainstream media coverage is probably how most people encounter fake news stories. This synthesis identifies why mainstream outlets cover falsehoods (journalists' professional duties, news values, peer psychology, technical infrastructure) and explores how mainstream coverage amplifies… Tsfati et al. · Annals of the International Communication… · 2020

Spread & networksPlatforms, policy & society Online misinformation about climate changeComprehensive literature review examining climate change misinformation from multiple disciplinary angles. Defines misinformation vs. disinformation, identifies networks of actors (scientists, governments, industry, media) spreading false claims, examines how social media characteristics (homophily, echo chambers, algorithmic bias)… Treen et al. · WIREs Climate Change · 2020

Bots & coordinated campaignsPlatforms, policy & society Troll Factories: Manufacturing Specialized Disinformation on TwitterLinvill and Warren analyze 9.04 million tweets from Russia's Internet Research Agency (IRA) spanning 2009–2018, identifying five specialized account categories—Right Troll, Left Troll, News Feed, Hashtag Gamer, and Fearmonger—each with distinct behavioral patterns, temporal deployment strategies, and communication styles. The authors… Linvill & Warren · Political Communication · 2020

Bots & coordinated campaignsPlatforms, policy & society Cross-Platform State Propaganda: Russian Trolls on Twitter and YouTube during the 2016 U.S. Presidential ElectionThis paper analyzes 1,052 Russian IRA Twitter accounts to understand their propaganda strategy during the 2016 U.S. presidential election. By examining hyperlinks to news stories and YouTube videos, the authors find IRA accounts linked to conservative sources more frequently than liberal ones, consistent with supporting Trump. Golovchenko et al. · The International Journal of Press/Politics · 2020

Bots & coordinated campaignsSpread & networks It takes a village to manipulate the media: coordinated link sharing behavior during 2018 and 2019 Italian electionsCoordinated networks of Facebook pages and groups substantially amplified problematic news during Italian elections: coordinated entities shared problematic domains 1.79×–2.22× more frequently than uncoordinated ones. The paper introduces a method to detect "coordinated inauthentic behavior" by identifying entities that… Giglietto et al. · Information, Communication & Society · 2020

Psychology & interventionsSpread & networks Misinformation and Morality: Encountering Fake-News Headlines Makes Them Seem Less Unethical to Publish and ShareFour preregistered experiments (N=2,587) show that repeatedly encountering fake-news headlines reduces moral condemnation of spreading them—regardless of whether people believe them. This "moral repetition effect" operates via fluency: familiar headlines feel less unethical to share, stronger inclinations to promote them online, and… Effron & Raj · Psychological Science · 2020

Spread & networksPlatforms, policy & society Evaluating the fake news problem at the scale of the information ecosystemUsing nationally representative data on mobile, desktop, and TV consumption spanning 2016–2018, the authors show that fake news comprises only 0.15% of Americans' daily media diet, while TV dominates news consumption by a 5:1 ratio over online. The paper challenges the widespread narrative of fake news as a pervasive crisis and suggests… Allen et al. · Science Advances · 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

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