Detecting Spam Movie Review Under Coordinated Attack With Multi-View Explicit and Implicit Relations Semantics Fusion

Spam reviews have long polluted review systems, undermining their industries. Detecting spam movie reviews faces some brand-new challenges compared to traditional spam detection. These include coordinated spamming attacks during premieres or at advance screenings. However, most of existing studies only use inherent relations among reviews, movies, and users, they do not fully exploit explicit and implicit relations between reviews in coordinated spamming attacks. To address these novel challenges, we propose a spam movie review detection method based on mining explicit and implicit relation semantics and fusing multi-view semantics. To the best of our knowledge, we are the first to enhance spam movie review detection by exploiting both explicit and implicit relations between reviews in coordinated spamming attacks. First, we build an explicit relation movie-review graph with movie synopses and high-quality external reviews. We extract movie factual knowledge embeddings using a Heterogeneous Graph Transformer (HGT) network. Next, we input the factual knowledge embeddings with corresponding review embeddings into a contrastive network to get review credibility features. Additionally, we build an implicit relation graph between reviews using metadata and semantic similarities. We extract relation-enhanced review semantics via another HGT network. Finally, we fuse the three review semantic features through an attention layer before making classification. Experiments show our method achieves higher performance and robustness over state-of-the-art methods.