Detecting Review Spammer Groups

With an increasing number of paid writers posting fake reviews to promote or demote some target entities through Internet, review spammer detection has become a crucial and challenging task. In this paper, we propose a three-phase method to address the problem of identifying review spammer groups and individual spammers, who get paid for posting fake comments. We evaluate the effectiveness and performance of the approach on a real-life online shopping review dataset from amazon.com. The experimental result shows that our model achieved comparable or better performance than previous work on spammer detection.

[1]  Min Yang,et al.  Ordering-Sensitive and Semantic-Aware Topic Modeling , 2015, AAAI.

[2]  Thomas L. Griffiths,et al.  The Author-Topic Model for Authors and Documents , 2004, UAI.

[3]  Bing Liu,et al.  Opinion spam and analysis , 2008, WSDM '08.

[4]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[5]  Arjun Mukherjee,et al.  Exploiting Burstiness in Reviews for Review Spammer Detection , 2021, ICWSM.

[6]  Andrei Z. Broder,et al.  On the resemblance and containment of documents , 1997, Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No.97TB100171).

[7]  Arjun Mukherjee,et al.  Spotting fake reviewer groups in consumer reviews , 2012, WWW.