IDEAS home Printed from https://ideas.repec.org/a/spr/infotm/v20y2019i3d10.1007_s10799-018-0288-1.html
   My bibliography  Save this article

An individual-group-merchant relation model for identifying fake online reviews: an empirical study on a Chinese e-commerce platform

Author

Listed:
  • Chuanming Yu

    (Zhongnan University of Economics and Law)

  • Yuheng Zuo

    (Zhongnan University of Economics and Law)

  • Bolin Feng

    (Zhongnan University of Economics and Law)

  • Lu An

    (Wuhan University)

  • Baiyun Chen

    (Zhongnan University of Economics and Law)

Abstract

During the online shopping process, customer reviews strongly influence consumers’ buying behaviour. Fake reviews are increasingly utilized to manipulate products’ reputations. Automatically and effectively identifying fake reviews has become a salient issue. This study proposes a novel individual-group-merchant relation model to automatically identify fake reviews on e-commerce platforms, which focuses on the behavioural characteristics of the stakeholders. Three groups of indicators are proposed, i.e., individual indicators, group indicators and merchant indicators. An unsupervised matrix iteration algorithm is utilized to calculate the fake degree values at individual, group and merchant levels. To validate the model, an empirical study of fake review identification on a Chinese e-commerce platform is implemented. A total of 97,804 reviews related to 93 online stores and 9558 different reviewers are randomly selected as the test data. The experimental results show that the F-measure values of the proposed method in identifying fake reviewers, online merchants and groups with reputation manipulation are 82.62%, 59.26% and 95.12%, respectively. The proposed method outperforms the traditional methods (e.g. Logistic Regression and K nearest neighbour) in identifying fake reviews. It suggests that the combinations of the behaviour indicators with content analysis can effectively improve the performances of the fake review identification. The proposed method is more scalable to large datasets and easier to be employed, as it does not require manual labelling training set and it eliminates the training of classification models. This study greatly contributes to purifying the Chinese environment of business competition and establishing a better regulatory mechanism for credit manipulation in China.

Suggested Citation

  • Chuanming Yu & Yuheng Zuo & Bolin Feng & Lu An & Baiyun Chen, 2019. "An individual-group-merchant relation model for identifying fake online reviews: an empirical study on a Chinese e-commerce platform," Information Technology and Management, Springer, vol. 20(3), pages 123-138, September.
    • Handle: RePEc:spr:infotm:v:20:y:2019:i:3:d:10.1007_s10799-018-0288-1
    DOI: 10.1007/s10799-018-0288-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10799-018-0288-1
    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/s10799-018-0288-1?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. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
    2. Ben Jabeur, Sami & Ballouk, Hossein & Ben Arfi, Wissal & Sahut, Jean-Michel, 2023. "Artificial intelligence applications in fake review detection: Bibliometric analysis and future avenues for research," Journal of Business Research, Elsevier, vol. 158(C).
    3. Banerjee, Snehasish & Chua, Alton Y.K., 2023. "Understanding online fake review production strategies," Journal of Business Research, Elsevier, vol. 156(C).

    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. Mariani, Marcello M. & Fosso Wamba, Samuel, 2020. "Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies," Journal of Business Research, Elsevier, vol. 121(C), pages 338-352.
    2. Yucheng Zhang & Zhiling Wang & Lin Xiao & Lijun Wang & Pei Huang, 2023. "Discovering the evolution of online reviews: A bibliometric review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    3. Irina Heimbach & Oliver Hinz, 2018. "The Impact of Sharing Mechanism Design on Content Sharing in Online Social Networks," Information Systems Research, INFORMS, vol. 29(3), pages 592-611, September.
    4. Mahesh Balan U & Saji K. Mathew, 2021. "Personalize, Summarize or Let them Read? A Study on Online Word of Mouth Strategies and Consumer Decision Process," Information Systems Frontiers, Springer, vol. 23(3), pages 627-647, June.
    5. Lanfei Shi & Siva Viswanathan, 2023. "Optional Verification and Signaling in Online Matching Markets: Evidence from a Randomized Field Experiment," Information Systems Research, INFORMS, vol. 34(4), pages 1603-1621, December.
    6. Wei Hong & Changyuan Zheng & Linhai Wu & Xujin Pu, 2019. "Analyzing the Relationship between Consumer Satisfaction and Fresh E-Commerce Logistics Service Using Text Mining Techniques," Sustainability, MDPI, vol. 11(13), pages 1-16, June.
    7. Park, Sangwon & Nicolau, Juan L., 2017. "Effects of general and particular online hotel ratings," Annals of Tourism Research, Elsevier, vol. 62(C), pages 114-116.
    8. Ting Li & Robert J. Kauffman & Eric van Heck & Peter Vervest & Benedict G. C. Dellaert, 2014. "Consumer Informedness and Firm Information Strategy," Information Systems Research, INFORMS, vol. 25(2), pages 345-363, June.
    9. Tingting Song & Jinghua Huang & Yong Tan & Yifan Yu, 2019. "Using User- and Marketer-Generated Content for Box Office Revenue Prediction: Differences Between Microblogging and Third-Party Platforms," Service Science, INFORMS, vol. 30(1), pages 191-203, March.
    10. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    11. Bi, Sheng & Liu, Zhiying & Usman, Khalid, 2017. "The influence of online information on investing decisions of reward-based crowdfunding," Journal of Business Research, Elsevier, vol. 71(C), pages 10-18.
    12. Azer, Jaylan & Anker, Thomas & Taheri, Babak & Tinsley, Ross, 2023. "Consumer-Driven racial stigmatization: The moderating role of race in online consumer-to-consumer reviews," Journal of Business Research, Elsevier, vol. 157(C).
    13. Yuan Sun & Shuyue Fang & Yujong Hwang, 2019. "Investigating Privacy and Information Disclosure Behavior in Social Electronic Commerce," Sustainability, MDPI, vol. 11(12), pages 1-27, June.
    14. Kaushik, Kapil & Mishra, Rajhans & Rana, Nripendra P. & Dwivedi, Yogesh K., 2018. "Exploring reviews and review sequences on e-commerce platform: A study of helpful reviews on Amazon.in," Journal of Retailing and Consumer Services, Elsevier, vol. 45(C), pages 21-32.
    15. Natalia Levina & Manuel Arriaga, 2014. "Distinction and Status Production on User-Generated Content Platforms: Using Bourdieu’s Theory of Cultural Production to Understand Social Dynamics in Online Fields," Information Systems Research, INFORMS, vol. 25(3), pages 468-488, September.
    16. Chan, Haksin & Yang, Morgan X. & Zeng, Kevin J., 2022. "Bolstering ratings and reviews systems on multi-sided platforms: A co-creation perspective," Journal of Business Research, Elsevier, vol. 139(C), pages 208-217.
    17. Yuheng Hu, 2021. "Characterizing Social TV Activity Around Televised Events: A Joint Topic Model Approach," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1320-1338, October.
    18. Tianshu Sun & Sean J. Taylor, 2020. "Displaying things in common to encourage friendship formation: A large randomized field experiment," Quantitative Marketing and Economics (QME), Springer, vol. 18(3), pages 237-271, September.
    19. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    20. Yabing Jiang & Hong Guo, 2012. "Design of Consumer Review Systems and Product Pricing," Working Papers 12-10, NET Institute.

    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:infotm:v:20:y:2019:i:3:d:10.1007_s10799-018-0288-1. 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.