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The Market for Fake Reviews

Author

Listed:
  • Sherry He

    (Anderson School of Management, University of California, Los Angeles, Los Angeles, California 90095)

  • Brett Hollenbeck

    (Anderson School of Management, University of California, Los Angeles, Los Angeles, California 90095)

  • Davide Proserpio

    (Marshall School of Business, University of Southern California, Los Angeles, California 90089)

Abstract

We study the market for fake product reviews on Amazon.com. Reviews are purchased in large private groups on Facebook and other sites. We hand-collect data on these markets and then collect a panel of data on these products’ ratings and reviews on Amazon, as well as their sales rank, advertising, and pricing policies. We find that a wide array of products purchase fake reviews, including products with many reviews and high average ratings. Buying fake reviews on Facebook is associated with a significant but short-term increase in average rating and number of reviews. We exploit a sharp but temporary policy shift by Amazon to show that rating manipulation has a large causal effect on sales. Finally, we examine whether rating manipulation harms consumers or whether it is mainly used by high-quality products in a manner like advertising or by new products trying to solve the cold-start problem. We find that after firms stop buying fake reviews, their average ratings fall and the share of one-star reviews increases significantly, particularly for young products, indicating rating manipulation is mostly used by low-quality products.

Suggested Citation

  • Sherry He & Brett Hollenbeck & Davide Proserpio, 2022. "The Market for Fake Reviews," Marketing Science, INFORMS, vol. 41(5), pages 896-921, September.
    • Handle: RePEc:inm:ormksc:v:41:y:2022:i:5:p:896-921
    DOI: 10.1287/mksc.2022.1353
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    Cited by:

    1. Alexei Parahonyak & Nick Vikander, 2024. "Strategic Use of Product Delays to Shape Word-of-Mouth Communication," Economics Series Working Papers 1032, University of Oxford, Department of Economics.
    2. Yassine Lefouili & Leonardo Madio, 2022. "The economics of platform liability," European Journal of Law and Economics, Springer, vol. 53(3), pages 319-351, June.
    3. Ishita Chakraborty & Joyee Deb & Aniko Oery, 2020. "When Do Consumers Talk?," Cowles Foundation Discussion Papers 2254R, Cowles Foundation for Research in Economics, Yale University, revised Mar 2021.
    4. Christoph Carnehl & Maximilian Schaefer & André Stenzel & Kevin Ducbao Tran, 2022. "Value for Money and Selection: How Pricing Affects Airbnb Ratings," Bristol Economics Discussion Papers 22/771, School of Economics, University of Bristol, UK.
    5. Salminen, Joni & Kandpal, Chandrashekhar & Kamel, Ahmed Mohamed & Jung, Soon-gyo & Jansen, Bernard J., 2022. "Creating and detecting fake reviews of online products," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    6. Emily West, 2021. "Review Pollution: Pedagogy for a Post-Truth Society," Media and Communication, Cogitatio Press, vol. 9(3), pages 144-154.
    7. Young Joon Park & Jaewoo Joo & Charin Polpanumas & Yeujun Yoon, 2021. "“Worse Than What I Read?” The External Effect of Review Ratings on the Online Review Generation Process: An Empirical Analysis of Multiple Product Categories Using Amazon.com Review Data," Sustainability, MDPI, vol. 13(19), pages 1-22, September.
    8. Daniel Ershov & Yanting, He & Stephan Seiler, 2023. "How Much Influencer Marketing Is Undisclosed? Evidence from Twitter," CESifo Working Paper Series 10743, CESifo.
    9. 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).
    10. Georgios Zervas & Davide Proserpio & John W. Byers, 2021. "A first look at online reputation on Airbnb, where every stay is above average," Marketing Letters, Springer, vol. 32(1), pages 1-16, March.
    11. Hajek, Petr & Hikkerova, Lubica & Sahut, Jean-Michel, 2023. "Fake review detection in e-Commerce platforms using aspect-based sentiment analysis," Journal of Business Research, Elsevier, vol. 167(C).
    12. 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).

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    More about this item

    Keywords

    word of mouth; electronic commerce; retailing;
    All these keywords.

    JEL classification:

    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law
    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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