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Cited by 1,263Digital Marketing and Social MediaSpam and Phishing DetectionDigital Platforms and Economics

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

Open access
Luca, Michael, Zervas, Georgios·2016·Management Science
DOI 10.1287/mnsc.2015.2304
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Abstract

Consumer reviews are now part of everyday decision making. Yet the credibility of these reviews is fundamentally undermined when businesses commit review fraud, creating fake reviews for themselves or their competitors. We investigate the economic incentives to commit review fraud on the popular review platform Yelp, using two complementary approaches and data sets. We begin by analyzing restaurant reviews that are identified by Yelp’s filtering algorithm as suspicious, or fake—and treat these as a proxy for review fraud (an assumption we provide evidence for). We present four main findings. First, roughly 16% of restaurant reviews on Yelp are filtered. These reviews tend to be more extreme (favorable or unfavorable) than other reviews, and the prevalence of suspicious reviews has grown significantly over time. Second, a restaurant is more likely to commit review fraud when its reputation is weak, i.e., when it has few reviews or it has recently received bad reviews. Third, chain restaurants—which benefit less from Yelp—are also less likely to commit review fraud. Fourth, when restaurants face increased competition, they become more likely to receive unfavorable fake reviews. Using a separate data set, we analyze businesses that were caught soliciting fake reviews through a sting conducted by Yelp. These data support our main results and shed further light on the economic incentives behind a business’s decision to leave fake reviews. This paper was accepted by Lorin Hitt, information systems.

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Cites

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  • Deepfakes can be used for widespread marketplace deception, with varied ramifications for both firms and consumers.

    “Thus, they can be used for the purpose of widespread marketplace deception, with varied ramifications for both firms and consumers (Europol, 2022; Luca & Zervas, 2016).”

    From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Luca & Zervas, 2016

    The Introduction describes fake reviews as a challenge for firms and consumers, and the Conclusion calls review fraud widespread. The paper studies fraudulent Yelp reviews, not deepfakes, so it does not support the claim that deepfakes can be used for this purpose. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Customer satisfaction ratings are questioned for their volatility, reliability, and validity.

    “The strong impact of these ratings, largely questioned about their volatility, the reliability and validity (Gerber & Krzywdzinski, 2019;Greenwood, Adjerid, & Angst, 2019;Luca & Zervas, 2016;Orlikowski & Scott, 2013;Rosenblat, Levy, Barocas, & Hwang, 2017), has been associated with workers' perception of job insecurity (Curchod et al., 2019;Veen et al., 2019).”

    From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Luca & Zervas, 2016

    Section 2.2, “Fake and Filtered Reviews” (page unavailable), says fraudulent and selectively solicited Yelp reviews can produce a positively biased review sample, supporting concern about the reliability and validity of those ratings. The paper does not establish that customer satisfaction ratings generally are volatile, nor does it study ratings of workers. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Cites

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Research works printed but not linked: 2

  1. Feng, Song, Ritwik Banerjee, Yejin Choi. 2012. Syntactic Stylometry for Deception Detection. Proceed- ings of the 50th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 171-175.
  2. Shleifer, Andrei. 2004. Does competition destroy ethical behavior? American Economic Review 94(2) 414-418.

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Authors
Luca, M., & Zervas, G.
Affiliations
  • Harvard University (US)
  • Boston University (US)
ORCID
Venue
Management Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Published
2016-12
Volume
62
Issue
12
Pages
3412-3427
ISSN
  • 0025-1909 (ISSN-L)
  • 1526-5501
DOI
10.1287/mnsc.2015.2304
OpenAlex
W3122315158
Cited by
1,263
References
20
Citations by year
2026: 114 · 2025: 113 · 2024: 110 · 2023: 141 · 2022: 157 · 2021: 141 · 2020: 105 · 2019: 128 · 2018: 81 · 2017: 76 · 2016: 34 · 2015: 40 · 2014: 18 · 2013: 3 · 2012: 1
FWCI
304.06
Type
journal article
Language
en
Open access
Locations
  • Management Science (publishedVersion)
  • SSRN Electronic Journal (acceptedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • OpenBU (Boston University) (submittedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
Funders
University of Pennsylvania
Topics
  • Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
Keywords
review fraud, fake reviews, Yelp, consumer reviews, online review platforms, restaurant reviews, corporate reputation, competition, restaurant chains, review filtering, economic incentives

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