GAIM: Graph-aware Feature Interactional Model for Spam Movie Review Detection

Lei Zhang, Xueqiang Song, Xiaoming Zhao, Yuwei Fang, Dong Li, Haizhou Wang

Abstract: Nowadays, more and more people decide whether to watch a certain movie by reading online movie reviews. Driven by large commercial interest, a growing number of spammers try to manipulate the online word-of-mouth of movies by publishing spam reviews. This has severely destroyed the credibility of movie review platforms and affected the healthy development of the lm industry. However, there is little research on the detection of spam movie reviews. Meanwhile, there are still great challenges for spam movie review detection, such as the lack of publicly available datasets, insufficient features, and low-performance detection models. In this paper, we firstly construct a dataset for spam movie reviews with our proposed labeling strategy and release it publicly. Secondly, we design 28 features to detect spam movie reviews, 13 of which are completely new features. Thirdly, in order to mine the characteristics of collusive attack behavior deeply, we propose a Graph-aware Feature Interactional Model (GAIM), which combines TextCNN, MLP (Multilayer Perceptron), and GAT (Graph Attention Network). After performance evaluation, the experimental results show that GAIM is more effective than the state-of-the-art baselines with an F1-score of 91.88% for spam movie review.