A Prediction Model of Malicious Information Dissemination Based on User Behavior Analysis

The spread of malicious information is highly destructive to society. In this article, we propose a prediction model for malicious information dissemination based on user behavior analysis. We analyze the potential connection between users and malicious information and the implicit relationship between features. First, considering the complex relationship between users and malicious information, and the fact that knowledge learning can effectively capture this dynamic, this article proposes a method to represent the implicit connection between users and information as vectors, in which the Trans-H knowledge representation learning algorithm is used to map the implicit relationships between users and malicious information onto a low-dimensional relational plane, achieving vectorized representation. Second, considering the interactions between different user features, this article uses a behavioral analysis network to solve the problem of insufficient expression ability of users' original features. This network can be used to predict users' preforwarding behaviors. Finally, time slicing is introduced for the timeliness of malicious information, and the diffusion stage of information is discretized, while the behavioral analysis network is populated according to the user's preforwarding behavior, and a kind of information-user feature matrix is constructed. Then, combined with the convolutional neural network on local spatiotemporal features, this article proposes a prediction model TS-CNN. Experiments show that on publicly available benchmark datasets, comparative experiments with existing popular methods reveal that our approach achieves significant improvements in accuracy, recall, and F1-score, thereby validating the effectiveness of our proposed relationship modeling and optimization method. The model cannot only effectively predict users' dissemination behaviors for information but also more realistically reflect the hidden factors that drive users' dissemination.