Achieving a generalizable early detection of fake news

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University of New Brunswick


In the era of widespread social media use, combatting the propagation of fake news is of paramount importance. Traditional methods for detecting fake news often struggle to adapt to evolving formats and require extensive data for early detection. To address these challenges, we propose the Multimodal Early Fake News Detection (MEFaND) approach, which leverages Graph Neural Networks (GNN) and Bidirectional Encoder Representations from Transformers (BERT). This approach enables early fake news detection using limited 5-hour propagation data and concise news content. MEFaND achieves an impressive F1-score of 0.99% (Politifact) and 0.96% (Gossipcop), outperforming existing methods. We also analyze user characteristics and study temporal and structural patterns in fake news propagation graphs. In addition, we introduce a User Susceptibility Assessment and Prediction model that employs user features to assess and predict their likelihood of spreading false information. Incorporating user actions, historical involvement, and profile traits, our model achieves 0.93% accuracy in user susceptibility assessment. This research addresses early fake news detection and user susceptibility analysis, contributing to effective strategies against misinformation on online social networks.