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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,…
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.
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.
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),…
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.
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,…
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…
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…
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…
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…
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…
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…
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…
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…
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.
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…
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…
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.