Detecting Opinion Spammer Groups Through Community Discovery and Sentiment Analysis

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9149)

Abstract

In this paper we investigate on detection of opinion spammer groups in review systems. Most existing approaches typically build pure content-based classifiers, using various features extracted from review contents; however, spammers can superficially alter their review contents to avoid detections. In our approach, we focus on user relationships built through interactions to identify spammers. Previously, we revealed the existence of implicit communities among users based upon their interaction patterns [3]. In this work we further explore the community structures to distinguish spam communities from non-spam ones with sentiment analysis on user interactions. Through extensive experiments over a dataset collected from Amazon, we found that the discovered strong positive communities are more likely to be opinion spammer groups. In fact, our results show that our approach is comparable to the existing state-of-art content-based classifier, meaning that our approach can identify spammer groups reliably even if spammers alter their contents.

Keywords

Opinion spammer groups Sentiment analysis Community discovery 

Notes

Acknowledgement

This work is supported in part by the National Science Foundation under the awards CNS-0747247, CCF-0914946 and CNS-1314229, and by an NSA Science of Security Lablet grant at North Carolina State University. We would also like to thank the anonymous reviewers for their valuable feedback.

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Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  1. 1.North Carolina State UniversityRaleighUSA
  2. 2.Qatar Computing Research InstituteDohaQatar

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