RumorGraphXplainer: Do Structures Really Matter in Rumor Detection

Abstract

The rise of social media has enabled individuals to rapidly share information, including rumors, which can have significant impacts on various domains. Traditional approaches to rumor control are impractical for social media platforms due to the volume and speed of information. Automated detection methods are needed that not only identify rumors early but also provide explanations for their decisions to protect free speech. Recent advancements in deep learning have shown promise in automating rumor detection. Graph-based models, such as bidirectional graph convolution network (Bi-GCN), capture propagation, and dispersion patterns to differentiate rumors from the truth. However, the interpretability of these deep learning models is a challenge. This article focuses on graph convolution networks (GCNs), which lack attention maps for easy model attribution but excel at capturing global structural features. We investigate the importance of graph structure in rumor detection using two GCN models on a real-world dataset, analyzing the learned latent propagation and dispersion features. To the best of our knowledge, this is the first study to explore GCNs in rumor detection and investigate the significance of graph structure in this task. Our research addresses three primary questions: 1) the primary contributors to GCN-based rumor detection models and their differences across models; 2) the importance of graph structure for accurate predictions in GCN-based models; and 3) the latent propagation and dispersion features learned by GCN-based detection models during the rumor detection process.