Exploring Graph-aware Multi-View Fusion for Rumor Detection on Social Media
This work addresses the challenge of improving rumor detection accuracy for social media platforms, though it appears incremental as it builds on existing multi-view methods by enhancing fusion techniques.
The paper tackles the problem of rumor detection on social media by proposing a novel multi-view fusion framework that uses Graph Convolutional Networks (GCN) and Convolutional Neural Networks (CNN) to better integrate features from different views, achieving state-of-the-art performance on two public datasets.
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 together. Experimental results on two public datasets demonstrate that our method outperforms state-of-the-art approaches.