MiSTR: A Multiview Structural-Temporal Learning Framework for Rumor Detection

With the rapid development of web technology, social media platforms have become a breeding ground for rumors. These rumors can threaten people's health, endanger the economy, and affect the stability of a country. In recent years, to mitigate the problem of rumors, computational detection of rumors has been studied, producing some promising early results. However, how to effectively capture the temporal information of retweet dynamics and the structural information of propagation structure is still neglected. In this article, we innovatively propose a novel Multiview Structural-Temporal Learning Framework for Rumor Detection, MiSTR, to jointly learn the temporal features of retweet dynamics, structural features of propagation graph, and the textual features of source tweet. More specifically, we utilize the timestamp encoding, and timestamp level and sequential level attention mechanisms to learn the temporal correlation among individual retweets. We propose two specific methods to learn the overall representation of propagation structure among users from both microscopic and mesoscopic perspectives. Encouraging empirical results on three real large-scale datasets demonstrate the superiority of our proposed method over the state-of-the-art approaches.