Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 240200116-8.doi: 10.11896/jsjkx.240200116

• Big Data & Data Science • Previous Articles     Next Articles

Rumor Detection Based on Similarity-enhanced Propagation Structure

LIN Yidi, LI Bicheng, YANG Haijun   

  1. College of Computer Science and Technology,Huaqiao University,Xiamen,Fujian 361021,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:LIN Yidi,born in 2000,postgraduate.His main research interests include na-tural language processing and graph neural network.
    LI Bicheng,born in 1970,Ph.D,professor,Ph.D supervisor.His main research interests include intelligent information processing,network ideological security,network public opinion monitoring and guidance,and big data analysis and mining.
  • Supported by:
    Joint Fund Project of Ministry of Education for Equipment Pre-research(8091B022150).

Abstract: The rapid rise of social media has led to the issue of rumor dissemination,causing negative impacts on society.Existing rumor detection algorithms mainly focus on the contents and propagation structures of news,but often overlook the potential influence of user preference similarity.When browsing posts,users are more likely to encounter information spreaded by other users with similar preferences,which can further fuel the spread of rumors.Moreover,existing research frequently neglects the diversity of propagation structures and the relationship between news content and its propagation structure.Different types of news should exhibit different propagation patterns.Therefore,this paper proposes a model named “SEPS”,which aims to establish connections between users with similar preferences and then categorize propagation structures into various forms to extract features of diffe-rent propagation patterns.Finally,by introducing contrastive learning and co-attention modules,the model enhances the correlation between news content and propagation structure.Experiments demonstrate that the “SEPS” model can effectively detect rumors,and its performance outperforms that of the best baseline models.

Key words: Rumor propagation, Propagation structure, User preferences, Propagation patterns, Relevance

CLC Number: 

  • TP391
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