IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v23y2023i1d10.1007_s10660-020-09455-8.html
   My bibliography  Save this article

A cross-site comparison of online review manipulation using Benford’s law

Author

Listed:
  • Cheng Zhao

    (Peking University)

  • Chong Alex Wang

    (Peking University)

Abstract

There is a growing concern that online reviews are targets of systematic manipulation, and manipulated reviews serve purposes other than informing consumers. In this article, we report a cross-site comparison of the aggregate-level manipulation using Benford’s law to detect anomalies. Benford’s law states that digits in naturally occurring data follow a logarithmic distribution. Deviation from such distribution is considered as a sign of systematic manipulation. We empirically examine word-count distributions of reviews on a Chinese food delivery service platform (FDS), Dianping, Yelp, and Amazon. Our empirical analysis suggests, in general, word counts of online review contents do not obey Benford’s law, although Benford’s law holds among high-quality reviews. Deviation from Benford’s law is larger in emerging markets compared with mature online marketplaces. Further analyses reveal that positive reviews, especially positive and extreme reviews, exhibit more deviation from Benford’s law.

Suggested Citation

  • Cheng Zhao & Chong Alex Wang, 2023. "A cross-site comparison of online review manipulation using Benford’s law," Electronic Commerce Research, Springer, vol. 23(1), pages 365-406, March.
    • Handle: RePEc:spr:elcore:v:23:y:2023:i:1:d:10.1007_s10660-020-09455-8
    DOI: 10.1007/s10660-020-09455-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-020-09455-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10660-020-09455-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fengyi Lin & Lijuan Zhao & Liming Guan, 2014. "Window Dressing in Reported Earnings: A Comparison of High-Tech and Low-Tech Companies," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(S1), pages 254-264.
    2. Dina Mayzlin & Yaniv Dover & Judith Chevalier, 2014. "Promotional Reviews: An Empirical Investigation of Online Review Manipulation," American Economic Review, American Economic Association, vol. 104(8), pages 2421-2455, August.
    3. Young Kwark & Jianqing Chen & Srinivasan Raghunathan, 2014. "Online Product Reviews: Implications for Retailers and Competing Manufacturers," Information Systems Research, INFORMS, vol. 25(1), pages 93-110, March.
    4. Fengyi Lin & Lijuan Zhao & Liming Guan, 2014. "Window Dressing in Reported Earnings: A Comparison of High-Tech and Low-Tech Companies," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(1S), pages 254-264, January.
    5. David Giles, 2007. "Benford's law and naturally occurring prices in certain ebaY auctions," Applied Economics Letters, Taylor & Francis Journals, vol. 14(3), pages 157-161.
    6. Zike Cao & Kai-Lung Hui & Hong Xu, 2018. "When Discounts Hurt Sales: The Case of Daily-Deal Markets," Information Systems Research, INFORMS, vol. 29(3), pages 567-591, September.
    7. Nelson, Phillip, 1970. "Information and Consumer Behavior," Journal of Political Economy, University of Chicago Press, vol. 78(2), pages 311-329, March-Apr.
    8. Yacheng Sun & Xiaojing Dong & Shelby McIntyre, 2017. "Motivation of User-Generated Content: Social Connectedness Moderates the Effects of Monetary Rewards," Marketing Science, INFORMS, vol. 36(3), pages 329-337, May.
    9. Dezhi Yin & Sabyasachi Mitra & Han Zhang, 2016. "Research Note—When Do Consumers Value Positive vs. Negative Reviews? An Empirical Investigation of Confirmation Bias in Online Word of Mouth," Information Systems Research, INFORMS, vol. 27(1), pages 131-144, March.
    10. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    11. Yi Zhao & Sha Yang & Vishal Narayan & Ying Zhao, 2013. "Modeling Consumer Learning from Online Product Reviews," Marketing Science, INFORMS, vol. 32(1), pages 153-169, May.
    12. Brett Hollenbeck & Sridhar Moorthy & Davide Proserpio, 2019. "Advertising Strategy in the Presence of Reviews: An Empirical Analysis," Marketing Science, INFORMS, vol. 38(5), pages 793-811, September.
    13. Naveen Kumar & Liangfei Qiu & Subodha Kumar, 2018. "Exit, Voice, and Response on Digital Platforms: An Empirical Investigation of Online Management Response Strategies," Information Systems Research, INFORMS, vol. 29(4), pages 849-870, December.
    14. Zhijie Lin & Ying Zhang & Yong Tan, 2019. "An Empirical Study of Free Product Sampling and Rating Bias," Service Science, INFORMS, vol. 30(1), pages 260-275, March.
    15. Nelson, Philip, 1974. "Advertising as Information," Journal of Political Economy, University of Chicago Press, vol. 82(4), pages 729-754, July/Aug..
    16. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    17. Warut Khern-am-nuai & Karthik Kannan & Hossein Ghasemkhani, 2018. "Extrinsic versus Intrinsic Rewards for Contributing Reviews in an Online Platform," Information Systems Research, INFORMS, vol. 29(4), pages 871-892, December.
    18. Bin Gu & Jaehong Park & Prabhudev Konana, 2012. "Research Note ---The Impact of External Word-of-Mouth Sources on Retailer Sales of High-Involvement Products," Information Systems Research, INFORMS, vol. 23(1), pages 182-196, March.
    19. Chrysanthos Dellarocas & Charles A. Wood, 2008. "The Sound of Silence in Online Feedback: Estimating Trading Risks in the Presence of Reporting Bias," Management Science, INFORMS, vol. 54(3), pages 460-476, March.
    20. Muhammad Rifki Shihab & Audry Pragita Putri, 2019. "Negative online reviews of popular products: understanding the effects of review proportion and quality on consumers’ attitude and intention to buy," Electronic Commerce Research, Springer, vol. 19(1), pages 159-187, March.
    21. Paulo B. Goes & Mingfeng Lin & Ching-man Au Yeung, 2014. "“Popularity Effect” in User-Generated Content: Evidence from Online Product Reviews," Information Systems Research, INFORMS, vol. 25(2), pages 222-238, June.
    22. Duan, Wenjing & Gu, Bin & Whinston, Andrew B., 2008. "The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry," Journal of Retailing, Elsevier, vol. 84(2), pages 233-242.
    23. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    24. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    25. Lin, Fengyi & Lin, Li-Jung & Yeh, Chin-Chen & Wang, Teng-Shih, 2018. "Does the board of directors as Fat Cats exert more earnings management? Evidence from Benford’s law," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 158-170.
    26. George Judge & Laura Schechter, 2009. "Detecting Problems in Survey Data Using Benford’s Law," Journal of Human Resources, University of Wisconsin Press, vol. 44(1).
    27. Liming Guan & Fengyi Lin & Wenchang Fang, 2008. "Goal-Oriented Earnings Management: Evidence from Taiwanese Firms," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 44(4), pages 19-32, July.
    28. Rodriguez R.J., 2004. "First Significant Digit Patterns From Mixtures of Uniform Distributions," The American Statistician, American Statistical Association, vol. 58, pages 64-71, February.
    29. Steffen Zimmermann & Philipp Herrmann & Dennis Kundisch & Barrie R. Nault, 2018. "Decomposing the Variance of Consumer Ratings and the Impact on Price and Demand," Information Systems Research, INFORMS, vol. 29(4), pages 984-1002, December.
    30. Theodoros Lappas & Gaurav Sabnis & Georgios Valkanas, 2016. "The Impact of Fake Reviews on Online Visibility: A Vulnerability Assessment of the Hotel Industry," Information Systems Research, INFORMS, vol. 27(4), pages 940-961, December.
    31. Yi-Chun (Chad) Ho & Junjie Wu & Yong Tan, 2017. "Disconfirmation Effect on Online Rating Behavior: A Structural Model," Information Systems Research, INFORMS, vol. 28(3), pages 626-642, September.
    32. Chong (Alex) Wang & Xiaoquan (Michael) Zhang & Il-Horn Hann, 2018. "Socially Nudged: A Quasi-Experimental Study of Friends’ Social Influence in Online Product Ratings," Information Systems Research, INFORMS, vol. 29(3), pages 641-655, September.
    33. Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
    34. Michael Luca & Georgios Zervas, 2016. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Management Science, INFORMS, vol. 62(12), pages 3412-3427, December.
    35. David Godes & José C. Silva, 2012. "Sequential and Temporal Dynamics of Online Opinion," Marketing Science, INFORMS, vol. 31(3), pages 448-473, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    2. Tao Lu & May Yuan & Chong (Alex) Wang & Xiaoquan (Michael) Zhang, 2022. "Histogram Distortion Bias in Consumer Choices," Management Science, INFORMS, vol. 68(12), pages 8963-8978, December.
    3. Young Kwark & Gene Moo Lee & Paul A. Pavlou & Liangfei Qiu, 2021. "On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data," Information Systems Research, INFORMS, vol. 32(3), pages 895-913, September.
    4. Zibo Liu & Zhijie Lin & Ying Zhang & Yong Tan, 2022. "The Signaling Effect of Sampling Size in Physical Goods Sampling Via Online Channels," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 529-546, February.
    5. Peiyu Chen & Lorin M. Hitt & Yili Hong & Shinyi Wu, 2021. "Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application," Information Systems Research, INFORMS, vol. 32(4), pages 1470-1489, December.
    6. Christoph Schneider & Markus Weinmann & Peter N.C. Mohr & Jan vom Brocke, 2021. "When the Stars Shine Too Bright: The Influence of Multidimensional Ratings on Online Consumer Ratings," Management Science, INFORMS, vol. 67(6), pages 3871-3898, June.
    7. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    8. Pei-Yu Chen & Yili Hong & Ying Liu, 2018. "The Value of Multidimensional Rating Systems: Evidence from a Natural Experiment and Randomized Experiments," Management Science, INFORMS, vol. 64(10), pages 4629-4647, October.
    9. Warut Khern-am-nuai & Karthik Kannan & Hossein Ghasemkhani, 2018. "Extrinsic versus Intrinsic Rewards for Contributing Reviews in an Online Platform," Information Systems Research, INFORMS, vol. 29(4), pages 871-892, December.
    10. Ji Wu & Haichuan Zhao & Haipeng (Allan) Chen, 2021. "Coupons or Free Shipping? Effects of Price Promotion Strategies on Online Review Ratings," Information Systems Research, INFORMS, vol. 32(2), pages 633-652, June.
    11. Heeseung Andrew Lee & Angela Aerry Choi & Tianshu Sun & Wonseok Oh, 2021. "Reviewing Before Reading? An Empirical Investigation of Book-Consumption Patterns and Their Effects on Reviews and Sales," Information Systems Research, INFORMS, vol. 32(4), pages 1368-1389, December.
    12. Wei Chen & Bin Gu & Qiang Ye & Kevin Xiaoguo Zhu, 2019. "Measuring and Managing the Externality of Managerial Responses to Online Customer Reviews," Service Science, INFORMS, vol. 30(1), pages 81-96, March.
    13. Duan, Yongrui & Liu, Tonghui & Mao, Zhixin, 2022. "How online reviews and coupons affect sales and pricing: An empirical study based on e-commerce platform," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    14. Ana Babić Rosario & Kristine Valck & Francesca Sotgiu, 2020. "Conceptualizing the electronic word-of-mouth process: What we know and need to know about eWOM creation, exposure, and evaluation," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 422-448, May.
    15. Yue Guan & Yong Tan & Qiang Wei & Guoqing Chen, 2023. "When Images Backfire: The Effect of Customer-Generated Images on Product Rating Dynamics," Information Systems Research, INFORMS, vol. 34(4), pages 1641-1663, December.
    16. Marios Kokkodis & Theodoros Lappas, 2020. "Your Hometown Matters: Popularity-Difference Bias in Online Reputation Platforms," Information Systems Research, INFORMS, vol. 31(2), pages 412-430, June.
    17. Tingting Nian & Arun Sundararajan, 2022. "Social Media Marketing, Quality Signaling, and the Goldilocks Principle," Information Systems Research, INFORMS, vol. 33(2), pages 540-556, June.
    18. Jake Hoskins & Shyam Gopinath & J. Cameron Verhaal & Elham Yazdani, 2021. "The influence of the online community, professional critics, and location similarity on review ratings for niche and mainstream brands," Journal of the Academy of Marketing Science, Springer, vol. 49(6), pages 1065-1087, November.
    19. Juan Feng & Xin Li & Xiaoquan (Michael) Zhang, 2019. "Online Product Reviews-Triggered Dynamic Pricing: Theory and Evidence," Information Systems Research, INFORMS, vol. 30(4), pages 1107-1123, December.
    20. Zheng, Lili, 2021. "The classification of online consumer reviews: A systematic literature review and integrative framework," Journal of Business Research, Elsevier, vol. 135(C), pages 226-251.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:elcore:v:23:y:2023:i:1:d:10.1007_s10660-020-09455-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.