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Platforms, policy & societyLLMs & generative AI
Disinformation 2.0 in the Age of AI: A Cybersecurity PerspectiveDisinformation should be treated as a cybersecurity threat, particularly as AI advances enable its creation and dissemination at unprecedented scale. The paper introduces "disinformation 2.0"—where generative AI, perturbation techniques, detection evasion, and optimized spread strategies make false content more persuasive and harder to…
Detection methodsDatasets & evaluation
From Fake News to #FakeNews: Mining Direct and Indirect Relationships among Hashtags for Fake News DetectionHashtags reveal news credibility signals during propagation: fake news clusters with hashtags used primarily for misinformation, while true news uses hashtags associated with reliable content. The authors propose NewsTag, a language-independent semi-supervised algorithm that mines both direct co-occurrence and indirect relationships…
Spread & networksDetection methods
"This is Fake! Shared it by Mistake": Assessing the Intent of Fake News SpreadersThis work is the first to systematically assess whether individuals spread fake news intentionally or unintentionally. The authors propose an influence graph that captures psychological (internal) and social (external) factors driving unintentional spreading, validate it on two datasets with manually-annotated spreader intent, and…
Spread & networksPlatforms, policy & society
Right-Wing YouTube: A Supply and Demand PerspectiveYouTube has become the dominant platform for right-wing political content in the US, driven by supply-side affordances (low creation barriers, monetization, algorithmic recommendation) and demand-side audience appetite for alternative media. Using longitudinal analysis of creator clusters (Conservative, Alt-Lite, Alt-Right), the authors…
Spread & networksDatasets & evaluation
Prevalence of Health Misinformation on Social Media: Systematic ReviewSystematic review of 69 studies examining health misinformation prevalence across social media platforms. Vaccines, drugs, and smoking are the most-studied topics; Twitter is the primary platform for misinformation about smoking and substances, while vaccines are discussed broadly across platforms.
Spread & networksDatasets & evaluation
A Survey of Information Cascade Analysis: Models, Predictions, and Recent AdvancesThis comprehensive survey systematizes research on predicting information cascade growth and structure across social networks, covering feature-based models, generative models, and deep learning approaches. It provides a taxonomy of prediction methods across temporal, structural, user/item, and content features; evaluation metrics and…
Spread & networksPlatforms, policy & society
Prevalence of Health Misinformation on Social Media: Systematic ReviewThis systematic review of 69 studies characterizes health misinformation topics and their prevalence across social media platforms. Health misinformation was most prevalent on Twitter regarding smoking and drugs; vaccines, noncommunicable diseases, and pandemics were also common topics.
Spread & networksPsychology & interventions
The Gospel According to Q: Understanding the QAnon Conspiracy from the Perspective of Canonical InformationThis paper systematically studies the QAnon conspiracy theory by collecting and analyzing 4,961 unique Q drops from six aggregation sites. The authors find that aggregation sites show poor agreement on what constitutes a canonical Q drop, that Q drops are incoherent and low-quality relative to other social media platforms, and that…
Detection methodsSpread & networks
User Preference-aware Fake News DetectionProposes UPFD, which detects fake news by jointly modeling user endogenous preferences (from historical posts) and exogenous context (news propagation graphs) via GNNs. Shows confirmation bias drives news sharing decisions, and historical user behavior provides complementary signals to improve detection over content-only and graph-only…
Spread & networksDetection methods
Causal Understanding of Fake News Dissemination on Social MediaThe paper addresses the fundamental causal question: what user attributes cause susceptibility to share fake news? Rather than observing correlations, the authors use inverse propensity scoring (IPS) to learn unbiased embeddings of fake news sharing behavior, treating these embeddings as confounders in a causal model.
Psychology & interventionsSpread & networks
News literacy, social media behaviors, and skepticism toward information on social mediaA national survey of 788 Americans examines how news literacy relates to what people see and share on social media, and whether it predicts skepticism toward information shared in these spaces. The key finding: people with higher news literacy see and post less political content on social media, but when they do engage, they're…
Spread & networksPsychology & interventions
Shared partisanship dramatically increases social tie formation in a Twitter field experimentA Twitter field experiment with bot accounts reveals that shared partisanship causally drives social tie formation: users were nearly three times more likely to reciprocally follow accounts matching their political party, regardless of the account's partisan intensity. Critically, this preference was symmetric—Democrats and Republicans…
Psychology & interventionsSpread & networks
Cognitive reflection correlates with behavior on TwitterHigh scorers on the Cognitive Reflection Test (CRT)—a measure of analytical thinking—are more discerning social media users: they follow fewer accounts, share news from more reliable sources, and engage more with substantive topics like politics. The study identifies "cognitive echo chambers" where low-CRT users preferentially follow…
Spread & networksPlatforms, policy & society
Examining the consumption of radical content on YouTubeLarge-scale study of 309,813 U.S. users' YouTube and web browsing from 2016–2019 finds that news consumption on YouTube is dominated by mainstream, centrist sources. While far-right content exists and engages users intensely, it remains a small and stable share.
Spread & networksPlatforms, policy & society
The echo chamber effect on social mediaComparative analysis of 100+ million posts across Twitter, Facebook, Reddit, and Gab on controversial topics shows echo chambers operate differently by platform: Facebook and Twitter exhibit strong homophilic clustering and polarized information diffusion, while Reddit and Gab show single-sided communities without the biased diffusion…
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…
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,…
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…
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…
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…
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…
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…
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.