Interpretable Review Spammer Group Detection Model Based on Knowledge Distillation and Counterfactual Generation
Chenghang Huo, Yunfei Luo, Jinbo Chao, Fuzhi Zhang · Electronics · 2025
Spammer group detection is necessary for curbing collusive review spammers on online shopping websites. However, the current detection approaches ignore exploring deep-level suspicious user review relationships and learning group features with low discrimination, which affects detection performance. Furthermore, the interpretation of detection results is easily influenced by noise features and unimportant group structures, leading to suboptimal interpretation performance. Aimed at addressing these concerns, we propose an interpretable review spammer group detection model based on knowledge distillation and counterfactual generation. First, we analyze user review information to generate a suspicious user review relationship graph, combining a graph agglomerative hierarchical clustering approach to discover candidate groups. Second, we devise a knowledge distillation network to learn discriminative candidate group features for detecting review spammer groups. Finally, we design a counterfactual generation model to search important subgraph structures for interpreting the detection results. The experiments indicate that the improvements in our model’s Precision@k and Recall@k are among the top-1000 state-of-the-art solutions on the Amazon, YelpChi, YelpNYC, and YelpZip datasets, which are [13.37%, 72.63%, 37.46%, and 18.83%] and [17.34%, 43.81%, 41.22%, and 21.05%], respectively. The Fidelities of our interpretation results under different Sparsity are around 6%, 7%, 7%, and 6% higher than that of the state-of-the-art solutions on the Amazon, YelpChi, YelpNYC, and YelpZip datasets, respectively.