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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… Zhou et al. · WSDM '19 · 2019

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… Roozenbeek & van der Linden · Palgrave Communications · 2019

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… Shu et al. · arXiv · 2019

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,… Sitaula et al. · arXiv · 2019

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… Zhou & Zafarani · arXiv · 2019

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,… Karimi et al. · 27th International Conference on… · 2018

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… Nyhan · 2018

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… Green · 2018

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… Aral · 2018

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… Krishnamurthy et al. · arXiv · 2018

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… Brundage et al. · arXiv · 2018

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… Hamborg et al. · International Journal on Digital Libraries · 2018

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. Lee et al. · arXiv · 2018

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. Potthast et al. · arXiv · 2018

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. Afchar et al. · arXiv · 2018

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… Baly et al. · arXiv · 2018

Psychology & interventionsPlatforms, policy & society Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social MediaThis paper presents the first large-scale linguistic and psycholinguistic analysis of hate speech categorized by its target: directed (personal attacks) versus generalized (targeting groups). The authors analyze 28,318 directed and ~2,500 generalized hate tweets, revealing that directed hate is more informal, angry, and explicitly… ElSherief et al. · ICWSM · 2018

Datasets & evaluationSpread & networks Sampling the News Producers: A Large News and Feature Data Set for the Study of the Complex Media LandscapeIntroduces NELA2017, a dataset of 1,586 news articles from 92 diverse news sources over 7 months (April–October 2017), including mainstream outlets, hyper-partisan sources, satire, and deliberately false news sites. Computes 130 content-based linguistic and engagement features on each article, enabling large-scale comparative analysis of… Horne et al. · ICWSM 2018 · 2018

Detection methodsFact-checking & verification All-in-one: Multi-task Learning for Rumour VerificationThis paper proposes a multi-task learning framework for rumor verification that jointly trains veracity classification (the main task) with auxiliary tasks—rumor detection and stance classification. The approach outperforms single-task baselines on both RumourEval and PHEME datasets, demonstrating that sharing representations across… Kochkina et al. · arXiv · 2018

Deepfakes & multimodalDetection methods DeepFakes: a New Threat to Face Recognition? Assessment and DetectionGAN-based face-swapping (Deepfakes) pose a significant threat to face recognition systems: VGG and FaceNet achieve false acceptance rates of 85.62% and 95.00% on high-quality Deepfakes. The authors release the first public Deepfake video database (620 videos from 16 subject pairs) and show that image quality metrics with SVM achieve… Korshunov & Marcel · arXiv · 2018

Deepfakes & multimodalDetection methods In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye BlinkingDetects DeepFake videos by exploiting the absence of natural eye blinking patterns in AI-generated faces. Uses a Long-term Recurrent Convolutional Network (LRCN) combining CNN for feature extraction with LSTM-RNN for temporal modeling of eye state, achieving 0.99 AUC on authentic vs. synthesized videos—a forensic signal invisible to the… Li et al. · arXiv · 2018

Deepfakes & multimodalDetection methods Detecting GAN-generated Imagery using Color CuesThis paper identifies two forensic cues to distinguish GAN-generated images from real camera imagery by analyzing how generator networks process color. The first cue exploits overlapping color channel weights in the generator (unlike camera spectral sensitivities); the second leverages the fact that generator normalization suppresses… McCloskey & Albright · arXiv · 2018

Spread & networksPlatforms, policy & society Combating Fake News with Interpretable News Feed AlgorithmsThis position paper reviews fake news detection methods and argues that news feed algorithms are insufficiently transparent, enabling echo chambers and filter bubbles that amplify misinformation. The authors propose interpretable and explainable news feed algorithms as a solution, positioning transparency and user awareness as critical… Mohseni & Ragan · arXiv · 2018

Fact-checking & verificationDetection methods DeClareE: Debunking Fake News and False Claims using Evidence-Aware Deep LearningDeClareE is an end-to-end neural network for evidence-aware credibility assessment of arbitrary natural-language claims. The model combines claim and evidence article representations using bidirectional LSTMs with claim-specific attention, automatically discovering which web articles support or refute a claim without hand-crafted… Popat et al. · arXiv · 2018