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Spread & networksDetection methods
Studying Fake News via Network Analysis: Detection and MitigationThis chapter surveys network-based approaches to fake news detection and mitigation on social media, organizing fake news dissemination along three dimensions (content, social, temporal) and introduces methods exploiting network structure: homogeneous networks (friendship, diffusion, credibility) and heterogeneous networks (knowledge,…
Fact-checking & verificationDatasets & evaluation
FEVER: A Large-Scale Dataset for Fact Extraction and VERificationThis paper introduces FEVER, a large-scale dataset of 185,445 human-verified claims with Wikipedia evidence for training fact verification systems. Claims are generated from Wikipedia and labeled as SUPPORTED, REFUTED, or NOT ENOUGH INFO; the paper presents a three-stage baseline pipeline (document retrieval, sentence selection, textual…
Fact-checking & verificationDatasets & evaluation
The Fact Extraction and VERification (FEVER) Shared TaskThis paper presents results of the first FEVER shared task competition, which challenged 23 teams to classify whether human-written factoid claims could be SUPPORTED or REFUTED using evidence retrieved from Wikipedia. The best system achieved 64.21% FEVER score, demonstrating the difficulty of joint evidence retrieval and natural…
Deepfakes & multimodalDetection methods
Exposing Deep Fakes Using Inconsistent Head PosesDeepfakes splice a synthesized face into an original image, introducing landmark misalignment errors invisible to the eye but detectable through 3D head pose estimation. The method compares head poses estimated from all facial landmarks versus only the central region; real faces show consistent poses while deepfakes exhibit large…
Detection methodsDeepfakes & multimodal
TI-CNN: Convolutional Neural Networks for Fake News DetectionThis paper proposes TI-CNN, a multimodal deep learning model that combines explicit and latent features from both text and images to detect fake news. Through analysis of 20,015 news items, the authors identify distinctive linguistic and visual patterns in fake versus real news, and demonstrate that their unified CNN-based approach…
Bots & coordinated campaignsPlatforms, policy & society
Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the WebLarge-scale empirical study of 2.7K Russian state-sponsored troll accounts and 27K tweets (Jan 2016–Sep 2017) reveals minimal direct Twitter influence but significant content amplification through RT news outlet. Trolls actively target political events, adopt multiple identities, concentrate geographically in USA/Germany/Eastern Europe,…
Spread & networksBots & coordinated campaigns
On the Origins of Memes by Means of Fringe Web CommunitiesLarge-scale empirical study of how image memes originate and propagate across fringe and mainstream Web communities. Analyzes 160M images posted to Twitter, Reddit, /pol/, and Gab (2016–2017) using perceptual hashing, clustering, and custom distance metrics.
Bots & coordinated campaignsSpread & networks
Who Let The Trolls Out? Towards Understanding State-Sponsored TrollsComparative analysis of 934 Russian and 770 Iranian state-sponsored trolls across Twitter and Reddit (10M posts) reveals distinct operational patterns: Russian trolls prioritized URL amplification (retweets, mentions) and pro-Trump messaging; Iranian trolls pursued regional and anti-Trump narratives motivated by real-world events. Both…
Spread & networksDatasets & evaluation
The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other ShenanigansComprehensive typology of the false information ecosystem on the Web, identifying eight types of false information (fabricated stories, propaganda, conspiracy theories, hoaxes, biased news, rumors, clickbait, satire), twelve actor categories (bots, criminal organizations, activists, governments, paid posters, journalists, useful idiots,…
Deepfakes & multimodalDetection methods
Two-Stream Neural Networks for Tampered Face DetectionThe paper proposes a two-stream neural network for detecting face tampering (face swaps) in images. One stream uses GoogLeNet to classify tampered faces by visual artifacts, while the second stream uses steganalysis features with a triplet network to capture low-level noise inconsistencies.
Detection methodsDatasets & evaluation
Detection and Resolution of Rumours in Social Media: A SurveyComprehensive survey of rumour detection and resolution in social media covering the entire pipeline: rumour definition and characterization, data collection strategies, annotation schemes, detection approaches for both emerging and long-standing rumours, tracking of rumour propagation, stance classification (support/deny/query/comment),…
Platforms, policy & societyFact-checking & verification
The Science of Fake NewsThis multidisciplinary review establishes a scientific framework for studying fake news, defining it as fabricated information that mimics news media form but lacks editorial norms. The paper identifies two mitigation strategies—empowering individuals through fact-checking and education, and structural platform interventions—while…
Spread & networksDetection methods
Rumor Detection on Twitter with Tree-structured Recursive Neural NetworksProposes two variants of recursive neural networks (RvNN)—bottom-up and top-down—that operate on propagation tree structures of tweets to detect rumors. Rather than treating tweets sequentially, the model learns representations that respect how tweets actually propagate in response threads.
Spread & networksBots & coordinated campaigns
Anatomy of an online misinformation networkA network analysis of misinformation and fact-checking spread during the 2016 US Presidential Election reveals stark segregation between the two communities on Twitter. The core of the network is dominated by misinformation spreaders; fact-checking nearly disappears in the densest regions.
Detection methodsDatasets & evaluation
Combating Fake News: A Survey on Identification and Mitigation TechniquesThis comprehensive survey of fake news detection and mitigation organizes existing methods into two broad categories: content-based approaches (linguistic and deep learning methods) and feedback-based approaches (exploiting propagation patterns, user responses, and temporal dynamics). The authors consolidate 23+ publicly available…
Datasets & evaluationSpread & networks
FakeNewsNet: A Data Repository with News Content, Social Context and Spatiotemporal Information for Studying Fake News on Social MediaFakeNewsNet is a multi-dimensional repository for fake news research on social media, integrating news content (linguistic and visual features), social context (user engagement, network structure, response patterns), and spatiotemporal information (location, timestamps). The dataset contains both fake and real news from PolitiFact and…
Spread & networksPlatforms, policy & society
Exposure to opposing views on social media can increase political polarizationThis field experiment tested whether exposure to opposing political views on social media reduces polarization. Twitter users (Republicans and Democrats) were randomly offered money to follow bots retweeting messages from opposing politicians and opinion leaders for one month.
Spread & networksPsychology & interventions
The small, disloyal fake news audience: The role of audience availability in fake news consumptionContrary to claims that fake news has reached a broad audience, this empirical analysis of comScore data shows the fake news audience is small and comprises heavy internet users. Applying the Law of Double Jeopardy from media theory, the authors show that fake news attracts disloyal audiences disproportionately from social platforms.
Spread & networksBots & coordinated campaigns
Influencers in Polarized Political Networks on TwitterThis paper identifies three types of influencers in polarized political Twitter conversations during Brazil's 2016 Dilma Rousseff impeachment: "opinion leaders" who shape discourse within ideological groups, "informational influencers" (media/institutions) drawn into one camp, and "activists" who amplify like-minded messages. The…
Bots & coordinated campaignsSpread & networks
Bots increase exposure to negative and inflammatory content in online social systemsSocial bots during the 2017 Catalan independence referendum strategically targeted influential humans to amplify negative and inflammatory content. Operating from network peripheries, bots coordinated sentiment-driven amplification that exacerbated polarization, with approximately one in three referendum-related tweets generated by bots.
Psychology & interventionsPlatforms, policy & society
Belief in conspiracy theories: Basic principles of an emerging research domainThis special issue introduction proposes four foundational principles for the emerging scientific study of conspiracy belief: conspiracy beliefs are consequential (with real impacts on health and relationships), universal (across cultures and historical periods), emotional (driven by sense-making rather than logic), and social (rooted in…
Deepfakes & multimodalDetection methods
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News DetectionThe paper proposes EANN, an adversarial neural network that detects fake news on newly emerged events by learning event-invariant features. An event discriminator forces the multi-modal feature extractor to remove event-specific features while preserving shared patterns across events.
Spread & networksDetection methods
This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real NewsFake news differs systematically from real news in stylistic features, especially titles. Contrary to the assumption that fake news mimics real news, it is actually more similar to satire: packed titles with fewer stop words and more proper nouns, combined with shorter, repetitive body text with simpler language.
Detection methodsSpread & networks
"News you don't believe": Audience perspectives on fake newsUsing focus groups and surveys across the US, UK, Spain, and Finland, Nielsen and Graves find that ordinary people see fake news as a spectrum rather than a binary distinction—encompassing poor journalism, political propaganda, misleading advertising, and fabricated content. Audiences experience the "fake news" problem as one of degree,…