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Complements or confounders? A study of effects of target and non-target features on online fraudulent reviewer detection

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  • Wang, Qiang
  • Zhang, Wen
  • Li, Jian
  • Ma, Zhenzhong

Abstract

The online review fraud process is an event in which a reviewer posts fraudulent reviews on an e-commerce platform with respect to a review target, such as a commodity or service. Extant studies on detecting fraudulent reviewers often rely on reviewer behavioral patterns and the textual content of reviews while ignoring the targets being reviewed. Based on the Goals-Plans-Action theory, we examine the relative importance of target features for fraudulent reviewer detection in comparison to that of non-target features. Target features refer to the features derived from the innate information related to the reviewed products or services while non-target features refer to the features derived from the acquired information related to the reviewed products or services. In this study, we analyze a sample from the Yelp.com dataset of restaurant reviews to help better understand the importance of target features and non-target features. The results suggest that using the combination of target features with non-target features can improve the performance of online fraudulent reviewer detection in comparison with using non-target features alone. Moreover, using the combination of target features with non-target features will further improve the performance of online fraudulent reviewer detection when we consider the non-target features as conditioned on the reviewed target features rather than treating them as independent of each other.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:jbrese:v:167:y:2023:i:c:s0148296323005593
    DOI: 10.1016/j.jbusres.2023.114200
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    References listed on IDEAS

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    1. 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.
    2. Fink, Lior & Rosenfeld, Liron & Ravid, Gilad, 2018. "Longer online reviews are not necessarily better," International Journal of Information Management, Elsevier, vol. 39(C), pages 30-37.
    3. Bigne, Enrique & Chatzipanagiotou, Kalliopi & Ruiz, Carla, 2020. "Pictorial content, sequence of conflicting online reviews and consumer decision-making: The stimulus-organism-response model revisited," Journal of Business Research, Elsevier, vol. 115(C), pages 403-416.
    4. Sherry He & Brett Hollenbeck & Davide Proserpio, 2022. "The Market for Fake Reviews," Marketing Science, INFORMS, vol. 41(5), pages 896-921, September.
    5. Zhuang, Mengzhou & Cui, Geng & Peng, Ling, 2018. "Manufactured opinions: The effect of manipulating online product reviews," Journal of Business Research, Elsevier, vol. 87(C), pages 24-35.
    6. Dandan Qiao & Shun-Yang Lee & Andrew B. Whinston & Qiang Wei, 2020. "Financial Incentives Dampen Altruism in Online Prosocial Contributions: A Study of Online Reviews," Information Systems Research, INFORMS, vol. 31(4), pages 1361-1375, December.
    7. Shun†Yang Lee & Liangfei Qiu & Andrew Whinston, 2018. "Sentiment Manipulation in Online Platforms: An Analysis of Movie Tweets," Production and Operations Management, Production and Operations Management Society, vol. 27(3), pages 393-416, March.
    8. 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.
    9. Kazim Topuz & Hasmet Uner & Asil Oztekin & Mehmet Bayram Yildirim, 2018. "Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network," Annals of Operations Research, Springer, vol. 263(1), pages 479-499, April.
    10. Zhihong Ke & De Liu & Daniel J. Brass, 2020. "Do Online Friends Bring Out the Best in Us? The Effect of Friend Contributions on Online Review Provision," Information Systems Research, INFORMS, vol. 31(4), pages 1322-1336, December.
    11. Delen, Dursun & Topuz, Kazim & Eryarsoy, Enes, 2020. "Development of a Bayesian Belief Network-based DSS for predicting and understanding freshmen student attrition," European Journal of Operational Research, Elsevier, vol. 281(3), pages 575-587.
    12. 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.
    13. 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.
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