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Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud

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  • Michael Luca

    ()
    (Harvard Business School, Negotiation, Organizations & Markets Unit)

  • Georgios Zervas

    ()
    (Boston University)

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    Abstract

    Review sites have become increasingly important sources of information for consumers. Because these reviews affect sales, businesses have the incentive to game the system by leaving positive reviews for themselves, or negative reviews for their competitors. Such review fraud undermines the trustworthiness of consumer reviews, and constitutes a major risk factor for review sites. In this paper, we investigate review fraud on the popular consumer review site Yelp. We construct a novel data set to analyze this problem, combining restaurant reviews with Yelp's algorithmic indicator of fake reviews. Using this imperfect indicator as a proxy, we develop an empirical methodology to identify the points in the life-cycle of a business during which review fraud is most prevalent. We find that a restaurant's changing reputation affects its decision to engage in review fraud. Specifically, a restaurant is more likely to seek a positive fake review when its reputation is weak, i.e., when it has few reviews, or it has recently received bad reviews. Consistent with theory, we find that chains are less likely than independent restaurants to engage in review fraud. We then turn our attention to negative review fraud, and find that increased competition by similar restaurants the driving force behind it.

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    Bibliographic Info

    Paper provided by Harvard Business School in its series Harvard Business School Working Papers with number 14-006.

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    Length: 31 pages
    Date of creation: Jul 2013
    Date of revision:
    Handle: RePEc:hbs:wpaper:14-006

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    References

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    1. Bryan Bollinger & Phillip Leslie & Alan Sorensen, 2011. "Calorie Posting in Chain Restaurants," American Economic Journal: Economic Policy, American Economic Association, vol. 3(1), pages 91-128, February.
    2. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    3. Ginger Zhe Jin & Phillip Leslie, 2009. "Reputational Incentives for Restaurant Hygiene," American Economic Journal: Microeconomics, American Economic Association, vol. 1(1), pages 237-67, February.
    4. Mark Duggan & Steven D. Levitt, 2000. "Winning Isn't Everything: Corruption in Sumo Wrestling," NBER Working Papers 7798, National Bureau of Economic Research, Inc.
    5. Weijia Dai & Ginger Z. Jin & Jungmin Lee & Michael Luca, 2012. "Optimal Aggregation of Consumer Ratings: An Application to Yelp.com," NBER Working Papers 18567, National Bureau of Economic Research, Inc.
    6. Michael Anderson & Jeremy Magruder, 2012. "Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online Review Database," Economic Journal, Royal Economic Society, vol. 122(563), pages 957-989, 09.
    7. Dina Mayzlin & Yaniv Dover & Judith A. Chevalier, 2012. "Promotional Reviews: An Empirical Investigation of Online Review Manipulation," NBER Working Papers 18340, National Bureau of Economic Research, Inc.
    8. Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
    9. Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
    10. David Godes & José C. Silva, 2012. "Sequential and Temporal Dynamics of Online Opinion," Marketing Science, INFORMS, vol. 31(3), pages 448-473, May.
    11. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
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    Cited by:
    1. Benjamin Edelman & Micahel Luca, 2014. "Digital Discrimination: The Case of Airbnb.com," Harvard Business School Working Papers 14-054, Harvard Business School.
    2. Dobrescu, Loretti I. & Luca, Michael & Motta, Alberto, 2013. "What makes a critic tick? Connected authors and the determinants of book reviews," Journal of Economic Behavior & Organization, Elsevier, vol. 96(C), pages 85-103.

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