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

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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.

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Acknowledgements

This study is supported by the National Natural Science Foundation of China (71373286 and 71603189) and the Ministry of Education of China (17JZD034).

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Authors and Affiliations

  1. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, 430073, Hubei Province, People’s Republic of China

    Chuanming Yu, Yuheng Zuo, Bolin Feng & Baiyun Chen

  2. School of Information Management, Wuhan University, Wuhan, 430072, Hubei Province, People’s Republic of China

    Lu An

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  1. Chuanming Yu
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  2. Yuheng Zuo
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  3. Bolin Feng
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  4. Lu An
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  5. Baiyun Chen
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Correspondence to Lu An.

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Yu, C., Zuo, Y., Feng, B. et al. An individual-group-merchant relation model for identifying fake online reviews: an empirical study on a Chinese e-commerce platform. Inf Technol Manag 20, 123–138 (2019). https://doi.org/10.1007/s10799-018-0288-1

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  • DOI: https://doi.org/10.1007/s10799-018-0288-1

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