Joint Stance and Rumor Detection in Hierarchical Heterogeneous Graph

Recently, large volumes of false or unverified information (e.g., fake news and rumors) appear frequently in emerging social media, which are often discussed on a large scale and widely disseminated, causing bad consequences. Many studies on rumor detection indicate that the stance distribution of posts is closely related to the rumor veracity. However, these two tasks are generally considered separately or just using a shared encoder/layer via multitask learning, without exploring the more profound correlation between them. In particular, the performance of existing methods relies heavily on the quality of hand-crafted features and the quantity of labeled data, which is not conducive to early rumor detection and few-shot detection. In this article, we construct a hierarchical heterogeneous graph by associating posts containing the same high-frequency words to facilitate the feature cross-topic propagation and jointly formulate stance and rumor detection as multistage classification tasks. To realize the updating of node embeddings jointly driven by stance and rumor detection, we propose a multigraph neural network framework, which can more flexibly capture the attribute and structure information of the context. Experiments on real datasets collected from Twitter and Reddit show that our method outperforms state-of-the-art by a large margin on both stance and rumor detection. And the experimental results also show that our method has better interpretability and requires less labeled data.

Automated summary

This research constructs a hierarchical heterogeneous graph to associate posts containing the same high-frequency words, facilitating feature propagation, and formulates stance and rumor detection as multistage classification tasks, proposing a multigraph neural network framework. The experiments demonstrate that the method outperforms state-of-the-art in both stance and rumor detection, with better interpretability and requiring less labeled data.