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Fact-checking & verificationDatasets & evaluation MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of ClaimsMultiFC is the largest publicly available real-world dataset of naturally occurring factual claims (34,918 claims) collected from 26 fact-checking websites, with rich metadata, evidence pages retrieved via Google Search, and entity linking to Wikipedia. The paper demonstrates that evidence ranking combined with veracity prediction… Augenstein et al. · arXiv · 2019

Bots & coordinated campaignsDeepfakes & multimodal Characterizing the Use of Images in State-Sponsored Information Warfare Operations by Russian Trolls on TwitterLarge-scale analysis of 1.8M images posted by ~3.6K Russian state-sponsored Twitter accounts (IRA-controlled). Russian trolls strategically deployed images and memes to maximize political influence, with political imagery 2+ times more effective than general content. Zannettou et al. · AAAI Conference on Web and Social Media · 2019

Bots & coordinated campaignsDatasets & evaluation Scalable and Generalizable Social Bot Detection through Data SelectionThis paper addresses scalability and generalization challenges in social bot detection by proposing a framework using only 20 user metadata features, enabling real-time analysis of the full Twitter stream. The key insight is that strategic data selection—training on a carefully chosen subset of 13 compiled datasets—achieves better… Yang et al. · arXiv · 2019

Bots & coordinated campaignsDatasets & evaluation Arming the public with artificial intelligence to counter social botsThis paper reviews social bot types, their impact on online discourse, and detection methods, then presents Botometer as a case study in how detection tools are used and interpreted by the public. The authors conducted a user experience survey (731 participants) revealing that while users find the tool useful, they struggle with… Yang et al. · arXiv · 2019

Detection methodsDatasets & evaluation Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda DetectionThis paper reports findings from a shared task on fine-grained propaganda detection in news articles, featuring two subtasks: fragment-level identification and classification into 18 propaganda techniques, and sentence-level binary propaganda detection. The task attracted 90 registered teams with 39 making submissions; winning systems… San Martino et al. · NLP4IF Workshop at EMNLP-IJCNLP · 2019

Deepfakes & multimodalDetection methods Recurrent Convolutional Strategies for Face Manipulation Detection in VideosThis paper proposes using recurrent convolutional networks to detect face manipulations (Deepfake, Face2Face, FaceSwap) in videos by exploiting temporal discrepancies across frames. Face preprocessing with alignment and bidirectional recurrent networks achieve state-of-the-art on FaceForensics++ with up to 4.55% improvement over prior… Sabir et al. · arXiv · 2019

Deepfakes & multimodalDetection methods FaceForensics++: Learning to Detect Manipulated Facial ImagesFaceForensics++ introduces a large-scale benchmark (1.8+ million manipulated images from 1,000+ videos) for detecting facial manipulations created with four state-of-the-art methods: Face2Face, FaceSwap, DeepFakes, and NeuralTextures. The paper comprehensively evaluates detection methods from stegananalysis to deep learning,… Rössler et al. · arXiv · 2019

Datasets & evaluationSpread & networks NELA-GT-2018: A Large Multi-Labelled News Dataset for The Study of Misinformation in News ArticlesNELA-GT-2018 is a large, engagement-independent collection of 713,534 news articles from 194 sources across 10 months (Feb–Nov 2018). It combines multi-source-level ground truth labels from eight independent assessment sites (NewsGuard, Pew Research, Wikipedia, OpenSources, Media Bias/Fact Check, AllSides, BuzzFeed News, PolitiFact)… Nørregaard et al. · arXiv · 2019

Datasets & evaluationDeepfakes & multimodal r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News DetectionFakeddit is a large-scale multimodal fake news detection dataset with over 1 million Reddit samples labeled with 2-way, 3-way, and 6-way classification schemes. Multimodal models combining text (BERT) and image (ResNet50) features achieve 86.0% accuracy on 6-way classification, demonstrating the importance of jointly leveraging text and… Nakamura et al. · arXiv · 2019

Detection methodsSpread & networks Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media ConversationsProposes Tree LSTM and Binarized Constituency Tree (BCTree) LSTM architectures enhanced with convolution units to classify stance and predict rumor veracity in social media conversation threads. The approach exploits the tree structure inherent in threaded conversations (source post with nested replies) and combines it with convolutional… Kumar & Carley · 57th Annual Meeting of the Association for… · 2019

Detection methodsDatasets & evaluation A Benchmark Study of Machine Learning Models for Online Fake News DetectionComprehensive benchmark of 19 machine learning approaches across three fake news datasets (LIAR, Fake or Real News, and an 80k-news combined corpus spanning politics, health, sports, and other topics). Key finding: BERT-based pre-trained models (especially RoBERTa) substantially outperform traditional and deep learning approaches,… Khan et al. · arXiv · 2019

LLMs & generative AIDetection methods Automatic Detection of Generated Text is Easiest when Humans are FooledIppolito et al. investigate the complementary abilities of humans and machines to detect machine-generated text from GPT-2 under various decoding strategies. They find that fine-tuned BERT detectors outperform humans (achieving 80%+ accuracy on long excerpts versus 71% for expert human raters), but detector performance is highly… Ippolito et al. · arXiv · 2019

Spread & networksDatasets & evaluation RumourEval 2019: Determining Rumour Veracity and Support for RumoursRumourEval 2019 is a shared task for rumour stance detection and veracity prediction on social media. Extending the 2017 edition, it introduces Reddit data alongside Twitter, provides datasets annotated for SDQC stance (Support/Deny/Query/Comment) and three-way veracity labels, and received 22 system submissions showing that modern… Gorrell et al. · 13th International Workshop on Semantic… · 2019

LLMs & generative AIDetection methods GLTR: Statistical Detection and Visualization of Generated TextGehrmann et al. present GLTR, a visual tool for detecting AI-generated text by analyzing statistical anomalies in language model output. The key insight is that language models sample from the head of the distribution (high-probability words), whereas humans use a wider vocabulary including low-probability choices. Gehrmann et al. · arXiv · 2019

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