Detecting Product Review Spammers using Rating Behaviors - LIM, Ee Peng (Institutional Knowledge at Singapore Management University, 2010)
This paper aims to detect users generating spam reviews or review spammers. We identify several characteristic be- haviors of review spammers and model these behaviors so as to detect the spammers. In particular, we seek to model the following behaviors. First, spammers may target specific products or product groups in order to maximize their im- pact. Second, they tend to deviate from the other reviewers in their ratings of products. We propose scoring methods to measure the degree of spam for each reviewer and apply them on an Amazon review dataset. We then select a sub- set of highly suspicious reviewers for further scrutiny by our user evaluators with the help of a web based spammer eval- uation software specially developed for user evaluation experiments. Our results show that our proposed ranking and supervised methods are e®ective in discovering spammers and outperform other baseline method based on helpfulness votes alone. We finally show that the detected spammers have more significant impact on ratings compared with the unhelpful reviewers.