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Deepfakes & multimodalPlatforms, policy & society Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in NewsExperimental study (N=2,005 UK respondents) testing how political deepfakes affect deception and trust in news. Deepfakes increased uncertainty about content, and that uncertainty mediated reduced trust in news on social media. Vaccari & Chadwick · Social Media + Society · 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 Psychology & interventionsPlatforms, policy & society Susceptibility to misinformation about COVID-19 around the worldAn international survey of 3,750 adults across five countries (UK, Ireland, USA, Spain, Mexico) measured susceptibility to COVID-19 misinformation and examined how it predicts public health behaviors. While majorities in each country did not find COVID-19 misinformation credible, higher susceptibility to misinformation was strongly… Roozenbeek et al. · Royal Society Open Science · 2020 Psychology & interventionsPlatforms, policy & society Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge InterventionTwo pre-registered experiments with 1,700+ U.S. adults show that a simple accuracy nudge — asking people to rate the accuracy of an unrelated headline before the main task — nearly tripled their ability to discriminate true from false COVID-19 headlines when deciding what to share on social media. The intervention is scalable, requires… Pennycook et al. · Psychological Science · 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 Fact-checking & verificationPlatforms, policy & society Fact-checking as risk communication: the multi-layered risk of misinformation in times of COVID-19COVID-19 misinformation should be viewed as a risk in its own right, not just a communication failure. Fact-checking efforts face three key challenges: defining misinformation differently across political subgroups, earning trust in an environment of low press credibility, and communicating amid genuine uncertainty. Krause et al. · Journal of Risk Research · 2020 Psychology & interventionsPlatforms, policy & society A digital media literacy intervention increases discernment between mainstream and false news in the United States and IndiaA randomized experiment tests Facebook's "Tips to Spot False News" intervention in the US and India, finding it increases discernment between mainstream and false news (26.5% in US, 17.3% in India online). The intervention reduces belief in false headlines more than it reduces belief in mainstream ones. Guess et al. · PNAS · 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 Bots & coordinated campaignsPlatforms, policy & society Assessing the Russian Internet Research Agency's impact on the political attitudes and behaviors of American Twitter users in late 2017Combining longitudinal survey data from 1,239 partisan Twitter users with nonpublic IRA account data, Bail et al. find no evidence that interaction with Russian Internet Research Agency accounts substantially impacted six measures of political attitudes (affective/ideological polarization) or behaviors (political engagement, network… Bail et al. · National Academy of Sciences · 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 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 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 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 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 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 Detection methodsDatasets & evaluation SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social MediaSemEval-2019 Task 6 reports on a shared task competition for identifying and categorizing offensive language in social media using the OLID dataset. Approximately 800 teams registered and 115 submitted results across three sub-tasks: offensive language detection (104 teams), offense type categorization (71 teams), and offense target… Zampieri et al. · 13th International Workshop on Semantic… · 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 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 Bots & coordinated campaignsDeepfakes & multimodal Characterizing the Use of Images in State-Sponsored Information Warfare Operations by Russian Trolls on TwitterLarge-scale analysis of 1.8M images posted by ~3.6K Russian state-sponsored Twitter accounts (IRA-controlled). Russian trolls strategically deployed images and memes to maximize political influence, with political imagery 2+ times more effective than general content. Zannettou et al. · AAAI Conference on Web and Social Media · 2019