DGTR: Dynamic Graph Transformer for Rumor Detection

Siqi Wei¹, Bin Wu^1*, Aoxue Xiang², Yangfu Zhu¹ and Chenguang Song¹

• ¹Beijing University of Posts and Telecommunications (BUPT), China
• ²Beijing University of Technology, China

Social media rumors have the capacity to harm public perception and social progress. The news propagation pattern is a key clue for detecting rumors. Existing propagation-based rumor detection methods represent propagation patterns as a static graph structure. They simply consider the structure information of news distribution in social networks and disregard the temporal information. The dynamic graph is an effective modeling tool for both the structural and temporal information involved in the process of news dissemination. Existing dynamic graph representation learning approaches struggle to capture the long-range dependence of structure and temporal sequence as well as the rich semantic association between full graph features and individual parts. We build a transformer-based dynamic graph representation learning approach for rumor identification DGTR to address the aforementioned challenges. We design a position embedding format for graph data such that the original transformer model can be utilized for learning dynamic graph representations. The model can describe the structural long-range reliance between dynamic graph nodes and the temporal long-range dependence between temporal snapshots by employing a self-attention mechanism. In addition, the cls token in transformer may model the rich semantic relationships between the complete graph and each subpart. Extensive experiments demonstrate the superiority of our model compared to the state of the art.