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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

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

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

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 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

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 methodsDatasets & evaluation Detection and Resolution of Rumours in Social Media: A SurveyComprehensive survey of rumour detection and resolution in social media covering the entire pipeline: rumour definition and characterization, data collection strategies, annotation schemes, detection approaches for both emerging and long-standing rumours, tracking of rumour propagation, stance classification (support/deny/query/comment),… Zubiaga et al. · ACM Computing Surveys · 2018

Deepfakes & multimodalDetection methods Two-Stream Neural Networks for Tampered Face DetectionThe paper proposes a two-stream neural network for detecting face tampering (face swaps) in images. One stream uses GoogLeNet to classify tampered faces by visual artifacts, while the second stream uses steganalysis features with a triplet network to capture low-level noise inconsistencies. Zhou et al. · arXiv · 2018

Spread & networksDatasets & evaluation The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other ShenanigansComprehensive typology of the false information ecosystem on the Web, identifying eight types of false information (fabricated stories, propaganda, conspiracy theories, hoaxes, biased news, rumors, clickbait, satire), twelve actor categories (bots, criminal organizations, activists, governments, paid posters, journalists, useful idiots,… Zannettou et al. · arXiv · 2018

Bots & coordinated campaignsSpread & networks Who Let The Trolls Out? Towards Understanding State-Sponsored TrollsComparative analysis of 934 Russian and 770 Iranian state-sponsored trolls across Twitter and Reddit (10M posts) reveals distinct operational patterns: Russian trolls prioritized URL amplification (retweets, mentions) and pro-Trump messaging; Iranian trolls pursued regional and anti-Trump narratives motivated by real-world events. Both… Zannettou et al. · arXiv · 2018

Spread & networksBots & coordinated campaigns On the Origins of Memes by Means of Fringe Web CommunitiesLarge-scale empirical study of how image memes originate and propagate across fringe and mainstream Web communities. Analyzes 160M images posted to Twitter, Reddit, /pol/, and Gab (2016–2017) using perceptual hashing, clustering, and custom distance metrics. Zannettou et al. · IMC 2018 · 2018

Bots & coordinated campaignsPlatforms, policy & society Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the WebLarge-scale empirical study of 2.7K Russian state-sponsored troll accounts and 27K tweets (Jan 2016–Sep 2017) reveals minimal direct Twitter influence but significant content amplification through RT news outlet. Trolls actively target political events, adopt multiple identities, concentrate geographically in USA/Germany/Eastern Europe,… Zannettou et al. · arXiv · 2018

Detection methodsDeepfakes & multimodal TI-CNN: Convolutional Neural Networks for Fake News DetectionThis paper proposes TI-CNN, a multimodal deep learning model that combines explicit and latent features from both text and images to detect fake news. Through analysis of 20,015 news items, the authors identify distinctive linguistic and visual patterns in fake versus real news, and demonstrate that their unified CNN-based approach… Yang et al. · arXiv · 2018

Deepfakes & multimodalDetection methods Exposing Deep Fakes Using Inconsistent Head PosesDeepfakes splice a synthesized face into an original image, introducing landmark misalignment errors invisible to the eye but detectable through 3D head pose estimation. The method compares head poses estimated from all facial landmarks versus only the central region; real faces show consistent poses while deepfakes exhibit large… Yang et al. · arXiv · 2018

Fact-checking & verificationDatasets & evaluation The Fact Extraction and VERification (FEVER) Shared TaskThis paper presents results of the first FEVER shared task competition, which challenged 23 teams to classify whether human-written factoid claims could be SUPPORTED or REFUTED using evidence retrieved from Wikipedia. The best system achieved 64.21% FEVER score, demonstrating the difficulty of joint evidence retrieval and natural… Thorne et al. · ACL 2018 Shared Task · 2018

Fact-checking & verificationDatasets & evaluation FEVER: A Large-Scale Dataset for Fact Extraction and VERificationThis paper introduces FEVER, a large-scale dataset of 185,445 human-verified claims with Wikipedia evidence for training fact verification systems. Claims are generated from Wikipedia and labeled as SUPPORTED, REFUTED, or NOT ENOUGH INFO; the paper presents a three-stage baseline pipeline (document retrieval, sentence selection, textual… Thorne et al. · arXiv · 2018

Spread & networksDetection methods Studying Fake News via Network Analysis: Detection and MitigationThis chapter surveys network-based approaches to fake news detection and mitigation on social media, organizing fake news dissemination along three dimensions (content, social, temporal) and introduces methods exploiting network structure: homogeneous networks (friendship, diffusion, credibility) and heterogeneous networks (knowledge,… Shu et al. · Book chapter · 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

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

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 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 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