Rumor detection driven by graph attention capsule network on dynamic propagation structures

Abstract

Rumor detection aims to judge the authenticity of posts on social media (such as Weibo and Twitter), which can effectively prevent the spread of rumors. While many recent rumor detection methods based on graph neural networks can be conducive to extracting the global features of rumors, each node of the rumor propagation structure learned from graph neural networks is considered to have multiple individual scalar features, which are insufficient for reflecting the deep-level rumor properties. To address the above challenge, we propose a novel model named graph attention capsule network on dynamic propagation structures (GACN) for rumor detection. Specifically, GACN consists of two components: a graph attention network enforced by capsule network that can encode static graphs into substructure classification capsules for mining the deep-level properties of rumor, and a dynamic network framework that can divide the rumor structure into multiple static graphs in chronological order for capturing the dynamic interactive features in the evolving process of the rumor propagation structure. Moreover, we use the capsule attention mechanism to combine the capsules generated from each substructure to focus more on informative substructures in rumor propagation. Extensive validation on two real-world datasets demonstrates the superiority of GACN over baselines.

A rumor detection model combining propagation structure and content features

“…Yan et al [15] modeled content information as graph structures, and used gated graph neural networks to learn semantic dependencies between different segments, and enhanced model representation by incorporating co-learning of rumor propagation features. Yang et al [16]…”