Integrating Temporal and Spatial Structures for Robust Rumor Detection in Social Networks

In today’s highly informalized society, the speed and scope of rumor dissemination pose a great threat to social stability and personal interests. Detecting rumors manually requires a lot of human effort. Therefore, automatic rumor detection has received significant attention. Recently, some researchers have focused on using propagation structural features to identify rumors. However, existing propagation structure-based methods either utilize only spatial features or only temporal features of propagation. Few models can effectively leverage both types of propagation structural features. This paper proposes a Source-Guided Temporal-Spatial joint rumor detection model (SGTS). SGTS dynamically divides the propagation process of an event into a series of temporal sub-events. Additionally, SGTS employs an information-level connection strategy that incorporates spatial structural features from previous temporal stages into the encoding of subsequent stages. In this way, SGTS can effectively capture the spatiotemporal features of propagation. Experimental results and in-depth analysis on commonly-used datasets demonstrate that SGTS achieves significant improvements over existing methods.