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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…
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.
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…
Detection methodsFact-checking & verification
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-CheckingThis paper performs a linguistic analysis of fake news across four categories—satire, hoaxes, propaganda, and trusted news—using lexical features (LIWC, subjectivity, hedging, intensifiers). Experiments show that fake news articles use more first-person pronouns, exaggerative language (superlatives, modal adverbs), and hedging, while…