Towards online anti-opinion spam: Spotting fake reviews from the review sequence

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

71 Scopus citations

Abstract

Detecting review spam is important for current e-commerce applications. However, the posted order of review has been neglected by the former work. In this paper, we explore the issue on fake review detection in review sequence, which is crucial for implementing online anti-opinion spam. We analyze the characteristics of fake reviews firstly. Based on review contents and reviewer behaviors, six time sensitive features are proposed to highlight the fake reviews. And then, we devise supervised solutions and a threshold-based solution to spot the fake reviews as early as possible. The experimental results show that our methods can identify the fake reviews orderly with high precision and recall.

Original languageEnglish
Title of host publicationASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
EditorsXindong Wu, Xindong Wu, Martin Ester, Guandong Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages261-264
Number of pages4
ISBN (Electronic)9781479958771
DOIs
StatePublished - 10 Oct 2014
Event2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014 - Beijing, China
Duration: 17 Aug 201420 Aug 2014

Publication series

NameASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Conference

Conference2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014
Country/TerritoryChina
CityBeijing
Period17/08/1420/08/14

Keywords

  • review analysis
  • review spam
  • reviewer behavior

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