Abstract
Recently opinion spam has been widespread on online review websites and has received significant research attention. Existing approaches to detecting online opinion spam can be categorized into three groups: (1) review behavior-based approaches, which use metadata associated with user review behavior and product profile, (2) language-based approaches, which focus on the characteristics of the language that the opinion spammers use, and (3) graph-based approaches, where various user-review-product networks are constructed for node connectivity and similarity analysis. Unfortunately, all the aforementioned approaches have their limitations. In this paper, we introduce a holistic system, DeepSpot, for fake review detection. DeepSpot recognizes the true and fake reviews based on both the real human-posted reviews and the synthetic machine-generated reviews leveraging sentiment classification. Specifically, DeepSpot augments the original reviews with synthetic reviews using the encoder-decoder neural networks trained by the positive and negative reviews, respectively. Extensive experiments on real-world data showed that DeepSpot outperformed the state-of-the-art approaches in terms of various effectiveness metrics for recognizing true and fake reviews.