Event-Driven Hypergraph Convolutional Network Based Rumor Detection Method

Rumor detection with propagation chain remains an important topic in the research of social network analysis. Previous studies mostly oversimplify this task as detecting rumor in propagation chains of comments. However, real-world news posts usually share complex user and event interactions, which can provide potential detection clues. Therefore, we propose an event-driven HyperGraph convolutional network (EHGCN), which makes the first attempt to model news, user and event correlations into a unified hypergraph convolutional network for rumor detection. Specifically, it exploits egocentric network of users to construct homogeneous users circles for enhancing user-aware rumor verification. Moreover, EHGCN jointly leverages the main event and sub-event patterns at the intra-event level for rumor detection. The extensive experimental results show that EHGCN could improve the performance of rumor detection. Studies also confirm that EHGCN can detect rumors at an early stage by acquiring rich user circle and event information.