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Spread & networksPlatforms, policy & society
Fake news on Twitter during the 2016 U.S. presidential electionFake news consumption on Twitter during the 2016 election was extremely concentrated: only 1% of users saw 80% of exposures, and 0.1% shared 80% of fake news. Conservative-leaning, older, highly politically engaged users drove most consumption.
Spread & networksPsychology & interventions
Less than you think: Prevalence and predictors of fake news dissemination on FacebookFake news dissemination on Facebook during the 2016 election was a rare behavior (8.5% of users shared at least one article), with clear demographic stratification: conservatives shared more articles from fake news domains (pro-Trump), but the strongest and most robust predictor was age—users over 65 shared nearly seven times as many…
Deepfakes & multimodalDetection methods
MVAE: Multimodal Variational Autoencoder for Fake News DetectionThe paper proposes MVAE, a variational autoencoder that learns shared representations of text and images in social media posts to detect fake news. The model jointly trains an encoder-decoder (to discover correlations across modalities) with a binary classifier.
Bots & coordinated campaignsPlatforms, policy & society
Coordinating a Multi-Platform Disinformation Campaign: Internet Research Agency Activity on Three U.S. Social Media Platforms, 2015 to 2017This paper analyzes Russian Internet Research Agency activity across Facebook, Twitter, and Reddit from 2015–2017 using vector autoregression models on 3,126 Facebook ads, 1.9M tweets, and 12,603 Reddit posts. Key finding: Reddit activity influenced subsequent Twitter activity (a "trial balloon" effect), but the IRA's Twitter engagement…
Spread & networksBots & coordinated campaigns
A Study of Misinformation in WhatsApp groups with a focus on the Brazilian Presidential ElectionsStudy of 130 public WhatsApp groups during the 2018 Brazilian presidential election found that 13.1% of shared links pointed to junk news sources, with 40% of links directing to YouTube—a platform used strategically to bypass platform moderation. Videos and images dominated message content (33% of all messages) and were heavily…
Fact-checking & verificationPlatforms, policy & society
Fighting Misperceptions and Doubting Journalists' Objectivity: A Review of Fact-checking LiteratureComprehensive literature review of 48 studies on political fact-checking, organized into three research areas: (1) fact-checking effectiveness for correcting misperceptions (mixed results, including backfire effects); (2) fact-checking as a profession (contested methods and reliability); (3) public opinion about fact-checking (mostly…
Spread & networksPlatforms, policy & society
Social Media for Political Campaigns: An Examination of Trump's and Clinton's Frame Building and Its Effect on Audience EngagementThis study analyzes how Trump and Clinton's 2016 campaigns framed messages on Twitter and Facebook, and how frame type affected audience engagement. Trump relied on conflict and negative emotional frames, while Clinton used morality and positive emotional frames.
Detection methodsSpread & networks
dEFEND: Explainable Fake News DetectionFake news detection on social media typically focuses on content alone, but user comments and reactions provide complementary signals. This paper proposes dEFEND, a hierarchical attention neural network that jointly encodes news sentences and user comments to both detect fake news and explain why a piece of news is detected as fake by…
Spread & networksDetection methods
Fake News: Fundamental Theories, Detection Strategies and ChallengesA comprehensive tutorial framework addressing fake news research across three dimensions: (1) fundamental theories from psychology, social science, economics, and forensics explaining human vulnerability to misinformation and why individuals participate in fake news activities; (2) detection strategies unified across four perspectives…
Psychology & interventionsPlatforms, policy & society
Fake news game confers psychological resistance against online misinformationThis large-scale empirical study demonstrates that a free ~15-minute online browser game teaching six common misinformation production techniques (polarisation, emotional manipulation, conspiracy theories, trolling, discrediting opponents, impersonation) significantly reduces perceived reliability of misinformation across N=15,000…
Detection methodsDatasets & evaluation
The Role of User Profiles for Fake News DetectionContent-only fake news detectors plateau because fake news is crafted to mimic real news textually. This paper systematically characterizes Twitter users who predominantly share fake versus real news across both partitions of FakeNewsNet (PolitiFact and GossipCop), comparing six explicit Twitter API attributes and four inferred implicit…
Detection methodsDatasets & evaluation
Credibility-based Fake News DetectionRather than relying solely on news text or propagation graphs, this paper frames fake news detection as a credibility assessment problem and operationalizes credibility through source-level signals (author count, coauthorship homophily, author publication history) and content-level signals (sentiments, domain vocabulary, argumentation,…
Spread & networksDetection methods
Network-based Fake News Detection: A Pattern-driven ApproachThis paper proposes a pattern-driven, network-based fake news detection model that identifies and quantifies four empirically grounded behavioral patterns in social networks: fake news attracts more spreaders, spreads farther, elicits stronger engagement, and is spread by denser networks than true news. These patterns are operationalized…
Detection methodsDatasets & evaluation
Multi-Source Multi-Class Fake News DetectionProposes MMFD, a multi-source multi-class fake news detection framework combining automated feature extraction (CNN-LSTM), interpretable multi-source fusion (attention mechanism), and fakeness discrimination. Achieves 38.81% accuracy on LIAR dataset across six classes of fakeness (true, mostly-true, half-true, barely-true, false,…
Psychology & interventionsFact-checking & verification
Why People Believe Fake News, Myths & Conspiracy TheoriesBrendan Nyhan surveys research on why people hold false beliefs about politics, health, and conspiracy theories despite exposure to corrective information. He identifies two core mechanisms: selective exposure (people seek out information matching their worldview) and selective acceptance (people dismiss contradictory information through…
Psychology & interventionsDetection methods
How technology can fight extremism and online harassmentYasmin Green, head of Jigsaw, presents two technological interventions addressing online harms rooted in human psychology. For radicalization: the "Redirect Method" (piloted 2016) uses online advertising to intercept users searching for extremist content (e.g., "How do I join ISIS?") and show them counter-propaganda videos from credible…
Spread & networksDeepfakes & multimodal
Don't Blame Bots, Fake News Is Spread by HumansA comprehensive talk covering the scale of misinformation campaigns on social media (ranging from Syrian hackers compromising the AP's Twitter account to Russian interference in the 2016 US election), empirical findings on how false news spreads faster than truth, and emerging threats from synthetic media (deepfakes) created via…
Deepfakes & multimodalDetection methods
A Deep Learning Approach for Multimodal Deception DetectionThis paper proposes a multimodal neural model for detecting deception in real-life courtroom trial videos by combining visual features from 3D-CNN, audio features from openSMILE, textual features from CNN on transcripts, and manual micro-expression annotations. The model achieves 96.14% accuracy and ROC-AUC of 0.9799, substantially…
Platforms, policy & societyDeepfakes & multimodal
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and MitigationA landmark 2018 report surveying the landscape of potential security threats from malicious uses of AI across three domains—digital, physical, and political security. The work proposes frameworks for forecasting AI-enabled attacks, identifies specific threat scenarios including AI-generated disinformation and deepfakes, and recommends…
Detection methodsLLMs & generative AI
Automated identification of media bias in news articles: an interdisciplinary literature reviewThis paper surveys automated and manual methods for identifying media bias in news articles, bridging social science and computer science approaches. It defines nine distinct forms of media bias and maps social science research onto computational techniques, finding that NLP-based methods are promising for automated detection but remain…
Detection methodsDatasets & evaluation
Comparative Studies of Detecting Abusive Language on TwitterConducts the first comprehensive benchmark of traditional machine learning and neural network models on the Hate and Abusive Speech on Twitter dataset (100K tweets). Bidirectional GRU networks with Latent Topic Clustering achieve the best performance (0.805 F1), outperforming traditional methods like logistic regression and SVM.
Detection methodsDatasets & evaluation
The Clickbait Challenge 2017: Towards a Regression Model for Clickbait StrengthClickbait detection is typically framed as binary classification, but teaser messages exhibit varying degrees of clickbaiting techniques. This paper organizes the Clickbait Challenge 2017, reformulating the task as regression to measure clickbait strength on a graded scale.
Deepfakes & multimodalDatasets & evaluation
MesoNet: A Compact Facial Video Forgery Detection NetworkThis paper proposes two lightweight CNN architectures (Meso-4 and MesoInception-4) to detect facial video forgeries created by Deepfake and Face2Face techniques. Operating at the mesoscopic level of image analysis, the networks achieve 98% detection accuracy for Deepfake and 95% for Face2Face videos under realistic compression conditions.
Fact-checking & verificationDatasets & evaluation
Predicting Factuality of Reporting and Bias of News Media SourcesThis paper frames predicting news media factuality and bias as classification tasks at the news outlet level rather than at the claim or article level. The authors extract 141 features from multiple sources (articles, Wikipedia, Twitter, URL structure, web traffic) and train SVM classifiers, creating a dataset of 1,066 websites manually…