Graph-Aware Multi-View Fusion for Rumor Detection on Social Media

Yang Wu, Jing Yang, Liming Wang, Zhen Xu

Abstract: Automatic detecting rumors on social media has become a challenging task. Previous studies focus on learning indicative clues from conversation threads for identifying rumorous information. However, these methods only model rumorous conversation threads from various views but fail to fuse multi-view features very well. In this paper, we propose a novel multi-view fusion framework for rumor representation learning and classification. It encodes the multiple views based on Graph Convolutional Networks (GCN) and leverages Convolutional Neural Networks (CNN) to capture the consistent and complementary information among all views and fuse them. Experimental results on two public datasets demonstrate that our method outperforms state-of-the-art approaches.