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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

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

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

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

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

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

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… Shu et al. · arXiv · 2018

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… Sharma et al. · ACM Transactions on Intelligent Systems and… · 2018

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. Wang et al. · KDD '18: The 24th ACM SIGKDD International… · 2018

Detection methodsDatasets & evaluation Automatic Detection of Fake NewsThis paper introduces two novel fake news detection datasets covering seven news domains and analyzes linguistic properties that distinguish fake from legitimate content. Using linguistic features including readability metrics, word categories, and punctuation patterns, the authors build classifiers achieving up to 78% accuracy and… Pérez-Rosas et al. · arXiv · 2017 Fact-checking & verificationDatasets & evaluation ClaimBuster: The first end-to-end fact-checking systemFoundational work introducing ClaimBuster, an end-to-end automated fact-checking system. Introduces a dataset of 23,533 human-annotated U.S. debate statements with check-worthiness labels, establishing baselines for automated claim check-worthiness detection and ranking. Hassan · VLDB · 2017

Detection methodsDatasets & evaluation A Survey on Hate Speech Detection using Natural Language ProcessingThis survey systematically reviews automatic hate speech detection methods, covering feature extraction techniques (surface features, embeddings, sentiment analysis, lexical resources, linguistic features, knowledge bases), classification approaches (mostly supervised learning with SVMs and neural networks), annotation practices, and key… Schmidt & Wiegand · Fifth International Workshop on Natural… · 2017

Datasets & evaluationFact-checking & verification Liar, Liar Pants on Fire: A New Benchmark Dataset for Fake News DetectionThis paper introduces LIAR, a benchmark dataset of 12,836 manually labeled short statements from POLITIFACT.COM spanning 2007–2016, addressing the critical lack of labeled data for fake news detection. Each statement is annotated with fine-grained veracity labels (pants-fire, false, barely-true, half-true, mostly-true, true) and rich… Wang · arXiv · 2017

Detection methodsSpread & networks Some Like it Hoax: Automated Fake News Detection in Social NetworksHoaxes on Facebook can be detected with 99% accuracy using user interaction patterns (who "liked" posts) rather than content analysis. Two algorithms—logistic regression and harmonic boolean crowdsourcing—achieve high accuracy even with minimal labeled training data, and knowledge transfers across different Facebook communities. Tacchini et al. · Technical Report, UC Santa Cruz · 2017

Fact-checking & verificationDatasets & evaluation Finding Streams in Knowledge Graphs to Support Fact CheckingKnowledge Stream (KS) is an unsupervised network-flow approach for computational fact-checking that treats a knowledge graph as a flow network. For a triple (subject, predicate, object), KS finds multiple paths connecting subject to object and uses edge capacities (based on relational similarity and node specificity) to compute a truth… Shiralkar et al. · arXiv · 2017

Bots & coordinated campaignsDatasets & evaluation Online Human-Bot Interactions: Detection, Estimation, and CharacterizationSocial media accounts controlled by software can mimic human behavior while executing coordinated campaigns. This paper develops a machine-learning framework extracting 1,150 behavioral features from Twitter account metadata, posting patterns, and social networks to classify bots with 95% accuracy on honeypot data. Varol et al. · arXiv · 2017

Detection methodsDatasets & evaluation Fake News Detection on Social Media: A Data Mining PerspectiveThis comprehensive survey reviews fake news detection on social media through a data mining lens. It frames the problem around two key aspects—characterization (understanding the psychological and social foundations of fake news) and detection (building computational methods using news content and social context features). Shu et al. · arXiv · 2017

Detection methodsLLMs & generative AI CSI: A Hybrid Deep Model for Fake News DetectionCSI is a three-module deep learning model that detects fake news by jointly modeling article text, temporal patterns of user engagement (response), and user behavior patterns (source). The Capture module uses LSTM to learn temporal response signals; the Score module learns user suspiciousness from co-engagement patterns; the Integrate… Ruchansky et al. · CIKM'17 · 2017

Detection methodsDatasets & evaluation A Stylometric Inquiry into Hyperpartisan and Fake NewsThis paper investigates whether writing style alone can distinguish hyperpartisan news from mainstream and fake news from real news. Using a manually fact-checked corpus of 1,627 articles from BuzzFeed, the authors find that hyperpartisan news is stylistically distinct from mainstream (F1 = 0.78), satire is easily distinguishable from… Potthast et al. · arXiv · 2017

Detection methodsSpread & networks Detect Rumors in Microblog Posts Using Propagation Structure via Kernel LearningThis paper addresses rumor detection in microblog posts by modeling information diffusion as propagation trees and proposing a kernel-based method (PTK) that captures high-order structural patterns differentiating rumors from non-rumors. The approach extends to context-sensitive variants (cPTK) and achieves superior early detection… Ma et al. · ACL · 2017

Spread & networksDatasets & evaluation Quantifying Controversy on Social MediaThis work presents a graph-based pipeline to measure controversy in social media discussions. The authors build conversation graphs from Twitter interactions (retweets, follows, mentions), partition them to identify opposing sides, and apply multiple controversy measures—with a random-walk-based metric (RWC) emerging as the most reliable… Garimella et al. · ACM Transactions on Social Computing · 2017

Spread & networksDatasets & evaluation SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumoursRumourEval is a shared task benchmark for detecting and verifying rumours in social media. It introduces two subtasks: (a) stance classification (SDQC: Support/Deny/Query/Comment) of replies to rumourous claims, and (b) veracity prediction (true/false) of source tweets. Derczynski et al. · 11th International Workshop on Semantic… · 2017

Bots & coordinated campaignsPlatforms, policy & society Detecting Bots on Russian Political TwitterAutomated Twitter accounts (bots) represent 50% of activity in Russian political discourse. Using supervised ensemble classifiers on 3.8M accounts and 18M tweets from 2014–2015, the authors achieve 95% precision in detecting bots while maintaining 75% recall. Stukal et al. · Big Data · 2017

Datasets & evaluationFact-checking & verification Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on TwitterThis paper compares hate speech detection systems trained on expert annotations (from feminist and anti-racism activists) versus amateur annotations (crowdsourced via CrowdFlower) on 6,909 tweets. Systems trained on expert annotations significantly outperform those trained on amateur data, and amateur annotators systematically label more… Waseem · 2016 EMNLP Workshop on Natural Language… · 2016