Fake Restaurant Review Detection Using Deep Neural Networks with Hybrid Feature Fusion Method

Online review websites all over the world have developed by leaps and bounds in the past decade, followed by more and more fake reviews that are very difficult to identify. This not only affects the decision-making of consumers, but also seriously undermines the fairness of the consumption market. Most existing research ignored the abundant metadata in reviews and its effective combination with the review text. In this paper, we firstly collect reviews from one of the most popular Chinese review platforms Dianping, and release two fake review datasets we construct, which makes up for the relative lack of datasets in this field. Afterward, we present some novel and effective features for fake review detection by analyzing the characteristics of the collected reviews. In addition, we propose a multimodal fake review detection model BAM (BERT + Attention + MLP), which takes the review text and the extracted features as input, and uses neural networks as well as multimodal fusion technology to realize the recognition of fake reviews. The experimental results show that BAM has low dependence on the size of the dataset. Compared with baseline models, it can be found that BAM has a better detection performance.