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SpamTracer: Manual Fake Review Detection for O2O Commercial Platforms by Using Geolocation Features

  • Ruoyu Deng
  • Na RuanEmail author
  • Ruidong Jin
  • Yu Lu
  • Weijia Jia
  • Chunhua Su
  • Dandan Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11449)

Abstract

Nowadays, O2O commercial platforms are playing a crucial role in our daily purchases. However, some people are trying to manipulate the online market maliciously by opinion spamming, a kind of web fraud behavior like writing fake reviews, due to fame and profits, which will harm online purchasing environment and should be detected and eliminated. Moreover, manual fake reviewers are more deceptive compared with old web spambots. Although several efficient methods were proposed in the fake review detection field, the manual fake reviewers are also evolving rapidly. They imitate to be benign users to control the velocity of review fraud actions, and deceive the detection system. Our investigation presented that geolocation factor is potential and can well reflect the distinctions between fake reviewers and benign users. In this research, we analyzed the geolocations of shops in reviews, found the distinct distribution features of those in fake reviewers and benign users, and proposed a SpamTracer model that can identify fake reviewers and benign users by exploiting an improved HMM (Hidden Markov Model). Our experiment demonstrated that SpamTracer could achieve 71% accuracy and 76% recall in the unbalanced dataset, outperforming some excellent classical approaches in the aspect of stability. Furthermore, SpamTracer can help to analyze the regularities of review fraud actions. Those regularities reflect the time and location in which online shops are likely to hire fake reviewers to increase their turnover. We also found that a small group of fake reviewers tend to work with plural shops located in a small business zone.

Keywords

O2O commercial platform Manual fake review detection Geolocation Hidden Markov Model 

Notes

Acknowledgments

This work is supported by: Chinese National Research Fund (NSFC) No. 61702330, Chinese National Research Fund (NSFC) Key Project No. 61532013, National China 973 Project No. 2015CB352401, JSPS Kiban(C) JP18K11298 and JSPS Kiban(B) JP18H0324.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ruoyu Deng
    • 1
  • Na Ruan
    • 1
    Email author
  • Ruidong Jin
    • 1
  • Yu Lu
    • 1
  • Weijia Jia
    • 1
  • Chunhua Su
    • 2
  • Dandan Xu
    • 3
  1. 1.Department of CSEShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Division of CSUniversity of AizuAizuwakamatsuJapan
  3. 3.China Unicom Research InstituteBeijingChina

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