[Objective] This paper aims to construct a tweet interaction hypergraph-based rumor detection model that integrates user propagation bias information to improve the accuracy of rumor detection. [Methods] A rumor detection model named UPBI_HGRD is proposed. The model integrates the user propagation bias information when obtaining the tweet node embedding representation, and constructs hyperedges based on user IDs to form a hypergraph that can reflect the interactive relationship of tweets. In addition, this paper proposes a tweet node-user hyperedge level multi-layer dual-level multi-head attention mechanism to focus on important tweet relationships, so as to effectively learn the embedding representation of nodes, and finally input it into a classifier to judge whether it is a rumor or not. [Results] The experimental results on three publicly available datasets show that the accuracy of the model reaches 94.57%, 97.82% and 94.76%, respectively, which is better than the existing baseline model, and has an excellent performance in early detection of rumors, which proves the effectiveness of the model. [Limitations] The limitation of the model in this paper is that the process of obtaining the tweet embedding representation that integrates the user propagation bias information and constructing the hypergraph has a certain time overhead. In the future, further research will be done to improve the time efficiency of the model. [Conclusions] The proposed method effectively improves the accuracy of rumor detection and provides a novel approach to identifying online rumors.
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