Embedding Learning with Relational Heterogeneous Information in Social Network Posts to Detect Malicious Behavior

Although social networking services (SNS) have enabled the free exchange of opinions and feelings, the posting of malicious content has increasingly become a problem. To solve this problem, malicious behavior detection methods based on posting behavior are being developed. Existing methods focus on semantic analysis of posts using natural language processing, and one existing approach uses graph neural networks to consider context from various elements, such as users, posts, hashtags, and entities. However, this approach does not adequately capture the complex patterns and interactions of SNS networks. In particular, it is insufficient to fully capture the complexity of heterogeneity between nodes and edges in an SNS network. In this paper, we propose a method for extended heterogeneous graph construction and an architecture for heterogeneous graph embedding learning. The proposed method focuses on and exploits the diverse heterogeneity of social networks, optimally integrates heterogeneous information from SNS posts, and analyzes the relationships in the data to improve the performance of malicious behavior detection. The effectiveness of the proposed method is demonstrated by evaluation on a newly collected large dataset.