Product reviews are now widely used by individuals and organizations for decision making (Litvin et al., 2008; Jansen, 2010) . And because of the profits at stake, people have been known to try to game the system by writing fake reviews to promote target products. As a result, the task of deceptive review detection has been gaining increasing attention. In this paper, we propose a generative LDA-based topic modeling approach for fake review detection. Our model can aptly detect the subtle differences between deceptive reviews and truthful ones and achieves about 95% accuracy on review spam datasets, outperforming existing baselines by a large margin.
REF use topic models to detect differences between deceptive and truthful topic-word distributions.
TopicSpam: a Topic-Model based approach for spam detection
ACL
Computer Science