Detecting fraudulent online Yelp reviews using K-L divergence and linguistic features
Christopher G. Harris · Procedia Computer Science · 2022
Nearly all businesses that sell a product or service directly to consumers will be reviewed online. The ubiquity and importance of these online reviews to business success has woefully produced numerous opportunities for fraud. In this paper, we introduce and combine two tactics to distinguish fake Yelp restaurant reviews from real ones. Using genuine Yelp reviews, we employ two tactics. First, we examine 12 features across 7 linguistic categories and exploit the difference between the two datasets, achieving an average precision of 0.854. Next, we draw upon the Zipf distribution of ranked terms in the real and fake reviews and employ the Kullback–Leibler (K-L) divergence technique to augment our ability to discriminate between real and fake reviews. We obtain a final average precision of 0.867, which is an improvement over other techniques on comparable datasets.