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Detecting Fake Reviews Based on Review-Rating Consistency and Multi-dimensional Time Series

  • Fang Youli
  • Wang HongEmail author
  • Di Ruitong
  • Wang Lutong
  • Jin Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11338)

Abstract

Online reviews can help people get more information about stores and products. The potential customers tend to make decisions according to them. However, driven by profit, spammers post fake reviews to mislead the customers by promoting or demoting target store. Previous studies mainly utilize the rating as an indicator for detection. However, these studies ignore an important problem that the rating cannot represent the sentiment accurately. In this paper, we propose a method of identifying fake reviews based on rating- review consistency and multi-dimensional time series. We first incorporate the sentiment analysis techniques into fake review detection. Then, we further discuss the relationship between ratings and fake reviews. In the end, this paper establishes an effective time series to detect fake reviews. Experimental results show that our proposed methods have good detection result and outperform state-of-art methods.

Keywords

Fake review Time series Emotional polarity Logic regression 

Notes

Acknowledgments

This work is supported by Guangzhou scholars project for universities of Guangzhou (No. 1201561613).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Fang Youli
    • 1
  • Wang Hong
    • 1
    Email author
  • Di Ruitong
    • 1
  • Wang Lutong
    • 1
  • Jin Li
    • 2
  1. 1.School of Information Science and EngineeringShandong Normal UniversityJinanChina
  2. 2.School of Computer Science and Educational SoftwareGuangzhou UniversityGuangzhouChina

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