Exploring Graph-aware Multi-View Fusion for Rumor Detection on Social Media

TL;DR

This paper introduces a novel multi-view fusion framework using GCN and CNN for improved rumor detection on social media, effectively combining multiple conversation perspectives.

Contribution

It proposes a new multi-view fusion approach that encodes conversation views with GCN and fuses them with CNN for better rumor classification.

Findings

Outperforms state-of-the-art methods on two datasets.

Effective multi-view feature encoding with GCN.

Successful fusion of views using CNN improves detection accuracy.

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 together. Experimental results on two public datasets demonstrate that our method outperforms state-of-the-art approaches.