Detecting opinion spammer groups and spam targets through community discovery and sentiment analysis

Euijin Choo, Ting Yu, Min Chi

Research output: Contribution to journalReview article

3 Citations (Scopus)


In this paper we investigate on detecting opinion spammer groups through analyzing how users interact with each other. More specifically, our approaches are based on 1) discovering strong vs. weak implicit communities by mining user interaction patterns, and 2) revealing positive vs. negative communities through sentiment analysis on user interactions. Through extensive experiments over various datasets collected from Amazon, we found that the discovered strong, positive communities are significantly more likely to be opinion spammer groups than other communities. Interestingly, while our approach focused mainly on the characteristics of user interactions, it is comparable to the state of the art content-based classifier that mainly uses various content-based features extracted from user reviews. More importantly, we argue that our approach can be more robust than the latter in that if spammers superficially alter their review contents, our approach can still reliably identify them while the content-based approaches may fail.

Original languageEnglish
Pages (from-to)283-318
Number of pages36
JournalJournal of Computer Security
Issue number3
Publication statusPublished - 1 Jan 2017



  • community discovery
  • Opinion spammer groups
  • sentiment analysis

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality
  • Hardware and Architecture
  • Computer Networks and Communications

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