MGAT-ESM: Multi-channel graph attention neural network with event-sharing module for rumor detection

Rumor detection is increasingly important in we-media. Most existing rumor detection models focus on mining features of text content, user profiles and propagation patterns. However, these models lack an efficient way to integrate information from multiple resources and have poor performance or low generalization on unseen data because they tend to capture event-specific features contained in visible data. In this study, we propose an end-to-end Multi-channel Graph ATtention network with Event-Sharing Module named MGAT-ESM. First, we parallelly build three subgraphs to model the propagation structures of source tweets and their responses, the relationships of source tweets and their words, and those of source tweets and their related users, respectively. We then design a path embedding method to learn the semantic information of propagation structures, and use graph attention neural network as a backbone to learn the representations of the other two subgraphs, and then aggregate the embedding representations of these three channel subgraphs with attention mechanism. Moreover, for learning event-invariant features in different rumors, we add an event-sharing module to the backbone network. Finally, we combine the learnt event-invariant features with the aggregated representations to get the final predictions. Experiments on two real-world benchmarks demonstrate that MGAT-ESM achieves the state-of-the-art performance. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

The study proposes an end-to-end Multi-channel Graph ATtention network with Event-Sharing Module for rumor detection, which achieves state-of-the-art performance.