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 the dynamic graph that is associated with coarse-grained temporal information. However, these methods fail to capture the long-term time dependency 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 also propose a memory-augmented attention to capture the long-term dependency and the depth of nodes in the propagation graph. Multi-head attention mechanism is applied between nodes in the memorybank and incremental nodes to iteratively update the memorybank, and the depth information of nodes is also considered. Finally, the propagation features of nodes in the memorybank are utilized for rumor detection. Experimental results on two public real-world datasets demonstrate the effectiveness of our model compared with the state-of-the-art baselines.

Detecting rumors on social media has become a crucial issue.Propagation structure-based methods have recently attracted increasing attention.When the propagation structure is represented by the dynamic graph, temporal information is considered.However, existing rumor detection models using dynamic graph typically focus only on coarse-grained temporal information and ignore the fine-grained temporal dynamics within individual snapshots and across snapshots.In this paper, we propose a novel Fine-Grained Dynamic Graph Neural Network (FGDGNN) model, which can incorporate the fine-grained temporal information of dynamic propagation graph in the intra-snapshot and dynamic embedding update mechanism in the inter-snapshots into a unified framework for rumor detection.Specifically, we first construct the edge-weighted propagation graph and the edge-aware graph isomorphism network is proposed.To obtain fine-grained temporal representations across snapshots, we propose an embedding transformation layer to update node embeddings.Finally, we integrate the temporal information in the inter-snapshots at the graph level to enhance the effectiveness of the proposed model.Extensive experiments conducted on three public real-world datasets demonstrate that our FGDGNN model achieves significant improvements compared with the state-of-the-art baselines.