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Bots & coordinated campaignsDetection methods Better Safe Than Sorry: an Adversarial Approach to improve Social Bot DetectionProposes GenBot, a novel genetic algorithm that synthesizes evolved versions of existing spambots by encoding behavioral timelines as "digital DNA" sequences and iteratively evolving them to resemble legitimate user behavior. Tests the evolved bots against three state-of-the-art detection systems and finds they largely evade detection… Cresci et al. · ACM Web Science Conference · 2019

Deepfakes & multimodalDetection methods FakeCatcher: Detection of Synthetic Portrait Videos using Biological SignalsFakeCatcher detects deepfakes by analyzing biological signals (photoplethysmography—blood flow patterns) extracted from facial regions. The key insight is that generative models fail to preserve spatial coherence and temporal consistency of these signals in authentic videos, making biological signal analysis a powerful forensic tool… Cifci et al. · IEEE Transactions on Pattern Analysis and… · 2019

Fact-checking & verificationPlatforms, policy & society Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News MediaRather than predicting media bias and trustworthiness separately, this paper jointly models both as ordinal regression tasks using Copula Ordinal Regression. The intuition is that hyper-partisan outlets tend to be less trustworthy, while centrist outlets are more factual. Baly et al. · arXiv · 2019

LLMs & generative AIDetection methods Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based DetectionThe paper demonstrates a practical attack where low-skilled adversaries can generate high-quality fake product reviews at scale by fine-tuning publicly available GPT-2 models on real review datasets. A two-step approach—generation plus BERT-based validation to preserve sentiment—produces reviews indistinguishable from genuine ones. Adelani et al. · arXiv · 2019

LLMs & generative AIDetection methods Defending Against Neural Fake NewsZellers et al. introduce GROVER, a conditional neural text generation model that can both generate and detect neural fake news. The paper shows that humans cannot reliably distinguish GROVER-generated propaganda from real human-written disinformation, rating machine-generated propaganda as more trustworthy (2.42/3 vs 2.19/3 for human… Zellers et al. · NeurIPS · 2019

Detection methodsDatasets & evaluation Beyond News Contents: The Role of Social Context for Fake News DetectionContent-only detection is insufficient because fake news is crafted to mimic real news. This paper argues that the tri-relationship among publishers, news pieces, and users forms rich social context for detection: partisan-biased publishers more likely publish fake news, and low-credibility users more likely share it. Shu et al. · ACM International Conference on Web Search… · 2019

Spread & networksDetection methods Hierarchical Propagation Networks for Fake News Detection: Investigation and ExploitationContent-only fake news detection has limited performance because fake news is deliberately crafted to mimic real news. This paper constructs hierarchical propagation networks from both macro-level (tweet/retweet cascade) and micro-level (reply conversation) granularity, extracts structural, temporal, and linguistic features from both… Shu et al. · AAAI · 2019

Spread & networksDetection methods Fake News Detection on Social Media using Geometric Deep LearningA graph neural network approach for fake news detection that exploits social network structure and news propagation patterns rather than content alone. The method integrates user profiles, social connections, and cascade dynamics to achieve 92.7% ROC AUC on Twitter data and can detect fake news within hours of propagation. Monti et al. · arXiv · 2019

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

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… Shu et al. · KDD 2019 · 2019

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. Sahly et al. · Social Media + Society · 2019

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… Nieminen & Rapeli · Political Studies Review · 2019

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… Machado et al. · The Web Conference · 2019

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… Lukito · Political Communication · 2019

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. Khattar et al. · World Wide Web Conference · 2019

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… Guess et al. · Science Advances · 2019

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. Grinberg et al. · Science · 2019

Spread & networksPlatforms, policy & society How Many People Live in Political Bubbles on Social Media? Evidence From Linked Survey and Twitter DataEady et al. link survey data from 1,496 Americans to Twitter data from 642,345 accounts and ~1.2 billion tweets to measure political polarization in media consumption. Rather than finding evidence of extreme "filter bubbles" where most people consume ideologically homogeneous content, they find substantial overlap in the accounts… Eady et al. · SAGE Open · 2019

Psychology & interventionsSpread & networks Cognitive attraction and online misinformationMisinformation spreads not because it is low-quality information constrained by digital media inefficiencies, but because it taps into general evolved cognitive preferences. A content analysis of 260 articles from 26 hoax websites shows that 86% contain at least one cognitive-appeal feature: negative framing (49%), threat descriptions… Acerbi · Palgrave Communications · 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 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

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

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 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