Detection of opinion spam based on anomalous rating deviation. Issue 22 (1st December 2015) - Savage, David; Zhang, Xiuzhen; Yu, Xinghuo; Chou, Pauline; Wang, Qingmai
Highlights: We propose a novel approach for detecting opinion spam based on reviewer ratings, with no need for text analysis. Our approach identifies reviewers having a significant number of reviews that disagree with majority opinion. Our approach requires significantly less running time than other, comparable approaches. Our approach is shown to successfully identify opinion spammers in synthetic and real world data sets. Abstract: The publication of fake reviews by parties with vested interests has become a severe problem for consumers who use online product reviews in their decision making. To counter this problem a number of methods for detecting these fake reviews, termed opinion spam, have been proposed. However, to date, many of these methods focus on analysis of review text, making them unsuitable for many review systems where accompanying text is optional, or not possible. Moreover, these approaches are often computationally expensive, requiring extensive resources to handle text analysis over the scale of data typically involved. In this paper, we consider opinion spammers manipulation of average ratings for products, focusing on differences between spammer ratings and the majority opinion of honest reviewers. We propose a lightweight, effective method for detecting opinion spammers based on these differences. This method uses binomial regression to identify reviewers having an anomalous proportion of ratings that deviate from the majority opinion. Experiments on real-world and synthetic data show that our approach is able to successfully identify opinion spammers. Comparison with the current state-of-the-art approach, also based only on ratings, shows that our method is able to achieve similar detection accuracy while removing the need for assumptions regarding probabilities of spam and non-spam reviews and reducing the heavy computation required for learning.