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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,…
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.
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…
Detection methodsFact-checking & verification
Generating Fact Checking ExplanationsThis paper presents the first study on automatically generating explanations for fact-checking veracity predictions. The authors show that jointly optimizing both veracity classification and explanation generation improves system performance.
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…
Fact-checking & verificationDetection methods
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake NewsProposes a framework to automatically find fact-checking articles that address claims in original tweets. Uses a multimodal attention network (MAN) combining textual and visual content from tweets with fact-checking article text to rank candidate fact-checks; demonstrates that multimodal retrieval substantially outperforms text-only…
Psychology & interventionsPlatforms, policy & society
Inoculating Against Fake News About COVID-19COVID-19 misinformation has proliferated rapidly on social media, undermining public health responses and vaccination uptake. Rather than relying on post-hoc fact-checking (which spreads slower than misinformation), the authors propose psychological inoculation—exposing people to weakened doses of misinformation techniques beforehand to…
Datasets & evaluationDetection methods
Fighting an Infodemic: COVID-19 Fake News DatasetThe authors present a manually annotated dataset of 10,700 COVID-19 related posts and articles (52% real, 48% fake) collected from social media and fact-checking websites. They benchmark four machine learning baselines (Decision Tree, Logistic Regression, SVM, Gradient Boost), achieving 93.32% F1-score with SVM, and release both the…
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…
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.
Deepfakes & multimodalFact-checking & verification
A Picture Paints a Thousand Lies? The Effects and Mechanisms of Multimodal Disinformation and Rebuttals Disseminated via Social MediaMultimodal disinformation (paired text and images) is perceived as more credible than text-only misinformation, but fact-checkers can overcome this effect. Importantly, the modality of fact-checks (visual or textual) matters less than their presence; fact-checks are more persuasive for people whose prior attitudes already oppose the…
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…
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,…
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…
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…
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…
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.
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.