Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks
This addresses the problem of misinformation spread on social media for users and platforms, with incremental improvements in modeling conversation structures.
The paper tackled rumor detection on Twitter by modeling conversation threads as undirected interaction graphs and using a claim-guided hierarchical graph attention network, achieving state-of-the-art performance on three datasets and showing superior early-stage detection capabilities.
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.