Detection of opinion spam based on anomalous rating deviation

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.