CoST: Comprehensive structural and temporal learning of social propagation for fake news detection

The widespread dissemination of fake news on social media platforms presents significant threats to individual privacy and societal stability. Traditional content-based fake news detection methods are vulnerable to sophisticated adversarial manipulations, while existing propagation-based approaches often fail to fully capture the complex structural and temporal dynamics of news diffusion. To address these limitations, this paper proposes CoST, a Comprehensive Structural and Temporal learning of social propagation for fake news detection that jointly models propagation structural patterns and multi-grained temporal dynamics. Specifically, for structural patterns, as the existing Graph Convolution Networks (GCN) based methods are inadequate to embed news’ propagation graphs that typically have hub structures and deep propagation paths, we propose a bi-directional Graph Attention LSTM module to capture the social hub and deep propagation patterns of news’ propagation graphs. Besides structural patterns, news propagation may also have complicated and diverse temporal patterns. To model the multi-grained temporal dynamics of propagation, we adopt a temporal-aware attention mechanism and a Transformer encoder-based self-attention mechanism to learn the local temporal interval and global propagation sequence features, respectively. Experimental results on several real-world datasets demonstrate the superiority of CoST over various state-of-the-arts, especially in the early detection of fake news.