Hypergraph-Based Rumor Detection Model Integrating User Propagation Bias Information
[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.
万方数据
维普
