SWAM: Adaptive Sliding Window and Memory-Augmented Attention Model for Rumor Detection

Mei Guo, Chen Chen, Chunyan Hou, Yike Wu, Xiaojie Yuan

Abstract: Detecting rumors on social media has become a critical task in combating misinformation. Existing propagation-based rumor detection methods often focus on the static propagation graph, overlooking that rumor propagation is inherently dynamic and incremental in the real world. Recently, propagation-based rumor detection models attempt to use dynamic graphs associated with coarse-grained temporal information. However, these methods fail to capture long-term temporal dependencies and detailed temporal features of propagation. To address these issues, we propose a novel adaptive Sliding Window and Memory-augmented Attention Model (SWAM) for rumor detection. The adaptive sliding window divides the sequence of posts into consecutive disjoint windows based on the propagation rate of nodes. We further introduce a memory-augmented attention mechanism to capture long-term dependencies and node depth information in the propagation graph. Specifically, a multi-head attention mechanism is applied between nodes in the memorybank and incremental nodes to iteratively update the memorybank, while explicitly incorporating node depth information. Finally, the propagation features of nodes stored in the memorybank are utilized for rumor detection. Experimental results on two public real-world datasets demonstrate that the proposed model outperforms state-of-the-art baselines.