A Malicious Information Popularity Prediction Model Based on User Influence

Abstract

In social networks, studying methods for predicting the popularity of malicious information can help improve the ability to predict online public opinion. This paper proposes a malicious information popularity prediction model based on user influence, targeting the cooperative adversarial nature of malicious information propagation, the problem of assessing user influence in malicious information propagation space, and the complexity of malicious information propagation space. First, regarding the cooperative adversarial nature of the malicious information propagation process, considering that user behavior is influenced by both malicious and positive information during the propagation process, evolutionary game theory and multiple linear regression are introduced, and internal and external behavioral factors of the user are synthesized to construct influential functions that quantify malicious information and positive information. Meanwhile, the influence matrix is introduced when quantifying information to construct a weighted malicious information propagation network further. Second, regarding the problem of assessing user influence in the malicious information propagation space, considering the advantages of PageRank in measuring the importance of web pages and combining the timeliness of malicious information propagation, an improved algorithm T-PageRank (Timeliness-PageRank) based on timeliness is proposed. Introducing the time decay factor into the PageRank algorithm effectively enhances the accuracy and timeliness of the influence assessment of malicious information propagation. Finally, regarding the complexity of the propagation space of malicious information and considering that Graph Attention Network (GAT) can effectively capture complex relationships between nodes, combined with user influence, a malicious information popularity prediction model based on GAT is constructed. The model learns the complex interaction between users by using GAT and updates the feature representation of users so that it can be used for subsequent malicious information popularity prediction tasks. The experiment shows that the model can not only accurately assess the influence of users but also effectively predict the popularity of malicious information propagation.