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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…
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…
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…
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.
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…
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…
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…
Deepfakes & multimodalDetection methods
MVAE: Multimodal Variational Autoencoder for Fake News DetectionThe paper proposes MVAE, a variational autoencoder that learns shared representations of text and images in social media posts to detect fake news. The model jointly trains an encoder-decoder (to discover correlations across modalities) with a binary classifier.
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 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…
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),…
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,…
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…
Spread & networksDetection methods
Rumor Detection on Twitter with Tree-structured Recursive Neural NetworksProposes two variants of recursive neural networks (RvNN)—bottom-up and top-down—that operate on propagation tree structures of tweets to detect rumors. Rather than treating tweets sequentially, the model learns representations that respect how tweets actually propagate in response threads.
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.
Detection methodsLLMs & generative AI
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on TwitterThis paper presents neural network models combining tweet content, social network interactions, and linguistic features (bias markers, subjectivity cues, moral foundations) to classify Twitter news posts as suspicious (propaganda, hoaxes, clickbait, satire) or verified. The models achieve 95% accuracy on binary classification and 0.7…
Detection methodsSpread & networks
"News you don't believe": Audience perspectives on fake newsUsing focus groups and surveys across the US, UK, Spain, and Finland, Nielsen and Graves find that ordinary people see fake news as a spectrum rather than a binary distinction—encompassing poor journalism, political propaganda, misleading advertising, and fabricated content. Audiences experience the "fake news" problem as one of degree,…
Spread & networksDetection methods
This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real NewsFake news differs systematically from real news in stylistic features, especially titles. Contrary to the assumption that fake news mimics real news, it is actually more similar to satire: packed titles with fewer stop words and more proper nouns, combined with shorter, repetitive body text with simpler language.
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…
Spread & networksDetection methods
Topological Recurrent Neural Network for Diffusion PredictionWang et al. propose Topo-LSTM, a novel LSTM architecture for modeling dynamic directed acyclic graphs (DAGs) to predict which node will be activated next in information cascades. The key insight is that information diffusion on networks should be modeled explicitly as diffusion topologies—graphs showing how information spreads from…
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.
Spread & networksDetection methods
Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTMThis paper proposes Branch-LSTM, a sequential neural model for rumour stance classification in Twitter conversations. By decomposing conversation threads into linear branches and modeling them with LSTM layers, the approach captures conversational context while assigning stance labels (Support/Deny/Query/Comment) to each tweet.
Bots & coordinated campaignsDetection methods
The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms RaceThis paper provides empirical evidence of a paradigm shift in spambot design on Twitter, where new "social spambots" closely mimic genuine user behavior and successfully evade detection. Neither Twitter's automated systems, human annotators, nor established academic detection tools can effectively identify these accounts; however,…
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…