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

Author

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

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    2. Banerjee, Snehasish & Chua, Alton Y.K., 2023. "Understanding online fake review production strategies," Journal of Business Research, Elsevier, vol. 156(C).
    3. Ajay Kumar & Ram D. Gopal & Ravi Shankar & Kim Hua Tan, 2022. "Fraudulent review detection model focusing on emotional expressions and explicit aspects : investigating the potential of feature engineering," Post-Print hal-03630420, HAL.
    4. 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).
    5. 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.

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