Employing Temporal Information and Propagation Structure to Detect Rumors

Due to the huge number of users and its easy access, rumors often spread widely and rapidly on social media. In order to monitor and discriminate rumor message dynamicly during propagation, automatic Rumor Detection (RD) has become an important task in NLP. This paper studies automatic event-level rumor detection on the web, which is a collection of posts in chronological order. Previous studies did not consider the connection between texts and propagation structure, which will miss useful information of temporal order or propagation structure. To address this issue, we propose a novel method Temporal Incorporating Structure Networks (TISN) to learn information from both plain text and propagation structure. Especially, we utilize transformer encoders to extract text information, and employ GCN (Graph Convolutional Network) to learn the patterns of rumor propagation. In addition, we enhance the influence of objective information by source tweet. Our method effectively achieves good performance by combining both structured and plain textual information. Experimental results on three datasets show the proposed method TISN achieves better performance than several baselines.