Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks

TL;DR

This paper introduces a novel claim-guided hierarchical graph attention network that models Twitter conversation threads as undirected graphs, significantly improving rumor detection accuracy and early-stage identification by leveraging social context and semantic relevance.

Contribution

It proposes a new graph-based neural network model that better captures conversation dynamics and semantic cues for rumor detection on social media.

Findings

Outperforms state-of-the-art rumor detection methods on three Twitter datasets.

Effective in early rumor detection stages.

Enhances social context representation in rumor classification.

Abstract

Rumors are rampant in the era of social media. Conversation structures provide valuable clues to differentiate between real and fake claims. However, existing rumor detection methods are either limited to the strict relation of user responses or oversimplify the conversation structure. In this study, to substantially reinforces the interaction of user opinions while alleviating the negative impact imposed by irrelevant posts, we first represent the conversation thread as an undirected interaction graph. We then present a Claim-guided Hierarchical Graph Attention Network for rumor classification, which enhances the representation learning for responsive posts considering the entire social contexts and attends over the posts that can semantically infer the target claim. Extensive experiments on three Twitter datasets demonstrate that our rumor detection method achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.