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The Detection of Fake Reviews in Bestselling Books: Exploration and Findings

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  • Kavita Krishnan

    (University of Houston-Victoria, USA)

  • Yun Wan

    (University of Houston-Victoria, USA)

Abstract

This study detected the possible manipulation of reviews for bestseller books. The authors first used clustering analysis to identify the cluster of bestselling books and patterns of manipulated reviews and ratings. They then used an artificial neural network to predict the possibility of review manipulation in bestselling books based on the patterns identified. The prediction outcome has an accuracy rate of 89%. They found that fake or manipulated reviews for bestselling books could be identified by analyzing abnormal rating fluctuations. The findings could help e-commerce platforms identify review manipulations and thereby help customers make prudent purchase decisions.

Suggested Citation

  • Kavita Krishnan & Yun Wan, 2021. "The Detection of Fake Reviews in Bestselling Books: Exploration and Findings," Journal of Electronic Commerce in Organizations (JECO), IGI Global, vol. 19(4), pages 64-79, October.
    • Handle: RePEc:igg:jeco00:v:19:y:2021:i:4:p:64-79
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    Cited by:

    1. Harrison-Walker, L. Jean & Jiang, Ying, 2023. "Suspicion of online product reviews as fake: Cues and consequences," Journal of Business Research, Elsevier, vol. 160(C).
    2. Tim Kollmer & Andreas Eckhardt & Victoria Reibenspiess, 2022. "Explaining consumer suspicion: insights of a vignette study on online product reviews," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1221-1238, September.

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