A burst-based unsupervised method for detecting review spammer groups

Abstract With the development of e-commerce, online shopping has become a part of people's life. As customers often refer to online product reviews for shopping, sellers often collude with review spammers in writing fake reviews to promote or demote target products. In particular, spammers working in groups are more harmful than individual attacks. To detect such spammer groups, previous researchers proposed some frequent item mining based algorithms and graph-based algorithms. In this paper, we propose a method called GSDB (Group Spam Detection algorithm based on review Burst). Our algorithm first locates target products attacked by spammers by detecting the abnormality of product rating distribution. As group spammers usually post many fake reviews within a short period, we design a burst-based algorithm that discovers candidate spammer groups in reviewbursts using the Kernel Density Estimation algorithm. As some innocent reviewers may coincidently review during the burst period, we formulate a variety of individual spam indicators to measure the spamicity of the reviewers to isolate the candidate spammer groups. Finally, we design a series of group spam indicators to measure and classify the spamicity of spammer groups. Experimental results show that our proposed GSDB algorithm outperforms state-of-the-art algorithms.