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A Tangled Web: Should Online Review Portals Display Fraudulent Reviews?

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  • Uttara M. Ananthakrishnan

    (Foster School of Business, University of Washington, Seattle, Washington 98195)

  • Beibei Li

    (Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Michael D. Smith

    (Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

The growing interest in online product reviews for legitimate promotion has been accompanied by an increase in fraudulent reviews. However, beyond algorithms for initial fraud detection, little is known about what review portals should do with fraudulent reviews after detecting them. In this paper, we address this question by studying how consumers respond to potentially fraudulent reviews and how review portals can leverage this knowledge to design better fraud management policies. To do this, we combine theoretical development from the trust literature with randomized experiments and statistical analysis using large-scale data from Yelp. We find that consumers tend to increase their trust in the information provided by review portals when the portal displays fraudulent reviews along with non-fraudulent reviews, as opposed to the common practice of censoring suspected fraudulent reviews. The impact of fraudulent reviews on consumers’ decision-making process increases with the uncertainty in the initial evaluation of product quality. We also find that consumers do not effectively process the content of fraudulent reviews (negative or positive). This result furthers the case for a decision heuristic that will incorporate the motivational differences between positive fraudulent reviews and negative fraudulent reviews to effectively aid consumers’ decision making.

Suggested Citation

  • Uttara M. Ananthakrishnan & Beibei Li & Michael D. Smith, 2020. "A Tangled Web: Should Online Review Portals Display Fraudulent Reviews?," Information Systems Research, INFORMS, vol. 31(3), pages 950-971, September.
    • Handle: RePEc:inm:orisre:v:31:y:2020:i:3:p:950-971
    DOI: 10.1287/isre.2020.0925
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    References listed on IDEAS

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    3. Fei Long & Yunchuan Liu, 2024. "Platform Manipulation in Online Retail Marketplace with Sponsored Advertising," Marketing Science, INFORMS, vol. 43(2), pages 317-345, March.
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    7. Kim, Jong Min & Park, Keeyeon Ki-cheon & Mariani, Marcello & Wamba, Samuel Fosso, 2024. "Investigating reviewers' intentions to post fake vs. authentic reviews based on behavioral linguistic features," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    8. Qiang Wang & Wen Zhang & Jian Li & Feng Mai & Zhenzhong Ma, 2024. "The devil is in the details! Effect of differentiated platform governance on online review manipulation," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    9. Wang, Qiang & Zhang, Wen & Li, Jian & Ma, Zhenzhong, 2023. "Complements or confounders? A study of effects of target and non-target features on online fraudulent reviewer detection," Journal of Business Research, Elsevier, vol. 167(C).
    10. Yasui, Yuta, 2021. "Controlling Fake Reviews," MPRA Paper 108177, University Library of Munich, Germany.
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