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Spread & networksDatasets & evaluation
Prevalence of Health Misinformation on Social Media: Systematic ReviewSystematic review of 69 studies examining health misinformation prevalence across social media platforms. Vaccines, drugs, and smoking are the most-studied topics; Twitter is the primary platform for misinformation about smoking and substances, while vaccines are discussed broadly across platforms.
Datasets & evaluationDetection methods
Assessing the Quality of the Datasets by Identifying Mislabeled SamplesDeep learning models with high capacity often memorize label noise, degrading generalization. This paper proposes AQUAVS, a supervised variational autoencoder that identifies mislabeled samples via a "noise score" computed from outliers in latent space representations.
Detection methodsDatasets & evaluation
Misinformation Detection on YouTube Using Video CaptionsThis work proposes using video captions as text features for classifying YouTube videos as misinformation, debunking misinformation, or neutral content. Across five conspiracy topics (vaccines, 9/11, chemtrails, moon landing, flat earth), the authors achieve 0.85–0.90 F1-score on three-class classification and 0.92–0.95 F1-score when…
Detection methodsLLMs & generative AI
A Heuristic-driven Uncertainty based Ensemble Framework for Fake News Detection in Tweets and News ArticlesAn ensemble-based fake news detection system combining multiple pre-trained language models (BERT, RoBERTa, XLNet, DeBERTa, ERNIE 2.0, ELECTRA) with a Statistical Feature Fusion Network (SFFN) and heuristic post-processing. The framework incorporates Monte Carlo Dropout for uncertainty estimation and leverages metadata features (URL…
Spread & networksDatasets & evaluation
A Survey of Information Cascade Analysis: Models, Predictions, and Recent AdvancesThis comprehensive survey systematizes research on predicting information cascade growth and structure across social networks, covering feature-based models, generative models, and deep learning approaches. It provides a taxonomy of prediction methods across temporal, structural, user/item, and content features; evaluation metrics and…
Detection methodsDatasets & evaluation
MDFEND: Multi-domain Fake News DetectionMost fake news detection methods focus on single-domain data, which leads to poor performance across different domains due to domain shift (varying word usage and propagation patterns). This paper introduces Weibo21, the first multi-domain fake news dataset from a single platform with 9 domains, and proposes MDFEND, a model using…
Fact-checking & verificationDatasets & evaluation
A Survey on Predicting the Factuality and the Bias of News MediaSurvey of media-level profiling methods for predicting news outlet factuality and political bias. Rather than checking individual claims or articles, the approach profiles entire news sources to enable early detection of unreliable content at publication time.
Detection methodsDatasets & evaluation
DEAP-FAKED: Knowledge Graph based Approach for Fake News DetectionDEAP-FAKED combines a biLSTM news encoder with knowledge graph embeddings to detect fake news using only article titles. The approach extracts named entities from titles, maps them to Wikidata using named entity disambiguation, and embeds them via ComplEx.
LLMs & generative AIDatasets & evaluation
TruthfulQA: Measuring How Models Mimic Human FalsehoodsIntroduces TruthfulQA, a benchmark of 817 questions across 38 categories designed to measure whether language models generate truthful answers. Tests GPT-3, GPT-Neo/J, GPT-2, and T5-based models, finding that larger models are less truthful—an inverse scaling phenomenon where the best model (GPT-3-175B) achieves only 58% truthfulness…
Detection methodsLLMs & generative AI
FakeBERT: Fake News Detection in Social Media with a BERT-based Deep Learning ApproachFakeBERT combines BERT (Bidirectional Encoder Representations from Transformers) with parallel 1D convolutional neural networks to detect fake news on social media. The model captures semantic and long-distance dependencies in text, achieving 98.90% accuracy on the real-world fake news dataset and substantially outperforming CNN and LSTM…
Fact-checking & verificationDatasets & evaluation
A Survey on Stance Detection for Mis- and Disinformation IdentificationComprehensive survey of stance detection for misinformation and disinformation. Stance detection—determining whether a text supports, denies, questions, or merely comments on a claim—plays dual roles: as a standalone fact-checking tool and as a component in broader verification pipelines.
Detection methodsLLMs & generative AI
KAN: Knowledge-aware Attention Network for Fake News DetectionMost fake news detection models rely on textual features and social context but ignore knowledge-level relationships among entities in news. This paper proposes KAN, which extracts entity mentions from news, aligns them with knowledge graphs (Wikidata), and uses two attention mechanisms (N-E and N-E2C) to measure entity and…
Detection methodsDatasets & evaluation
Cross-lingual COVID-19 Fake News DetectionThe paper addresses COVID-19 misinformation in low-resource languages by proposing CrossFake, a cross-lingual fake news detector trained on English COVID-19 news and applied to Chinese news via machine translation. The method slices long news texts into sub-text groups before BERT encoding to preserve fact-related information.
LLMs & generative AIDatasets & evaluation
All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated TextHuman evaluators cannot reliably distinguish machine-generated text from human-written text without training, focusing instead on superficial features like grammar and style. Three training approaches (instructions, examples, and comparisons) improve evaluator accuracy modestly, with example-based training showing the most improvement…
Deepfakes & multimodalDetection methods
A Survey on Multimodal Disinformation DetectionComprehensive survey of multimodal disinformation detection covering text, images, speech, video, network structure, and temporal information. Distinguishes factuality (whether content is false) from harmfulness (whether false content intends to harm), systematically reviews ~140 papers across different modalities and detection…
Detection methodsDeepfakes & multimodal
Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multimodal DataMost fake news detection models are trained on a single domain (e.g., politics) and fail when tested on others (e.g., entertainment, COVID-19). This paper proposes a multimodal framework that leverages both domain-specific and cross-domain knowledge to detect fake news across different domains.