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Resampling imbalanced data to detect fake reviews using machine learning classifiers and textual-based features

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Abstract

Fraudulent online sellers often collude with reviewers to garner fake reviews for their products. This act undermines the trust of buyers in product reviews, and potentially reduces the effectiveness of online markets. Being able to accurately detect fake reviews is, therefore, critical. In this study, we investigate several preprocessing and textual-based featuring methods along with machine learning classifiers, including single and ensemble models, to build a fake review detection system. Given the nature of product review data, where the number of fake reviews is far less than that of genuine reviews, we look into the results of each class in detail in addition to the overall results. We recognise from our preliminary analysis that, owing to imbalanced data, there is a high imbalance between the accuracies for different classes (e.g., 1.3% for the fake review class and 99.7% for the genuine review class), despite the overall accuracy looking promising (around 89.7%). We propose two dynamic random sampling techniques that are possible for textual-based featuring methods to solve this class imbalance problem. Our results indicate that both sampling techniques can improve the accuracy of the fake review class—for balanced datasets, the accuracies can be improved to a maximum of 84.5% and 75.6% for random under and over-sampling, respectively. However, the accuracies for genuine reviews decrease to 75% and 58.8% for random under and over-sampling, respectively. We also discover that, for smaller datasets, the Adaptive Boosting ensemble model outperforms other single classifiers; whereas, for larger datasets, the performance improvement from ensemble models is insignificant compared to the best results obtained by single classifiers.

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Acknowledgements

The first author would like to acknowledge financial support from the Indonesian Endowment Fund for Education (LPDP), Ministry of Finance, and the Directorate General of Higher Education (DIKTI), Ministry of Education and Culture, Republic of Indonesia.

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Authors and Affiliations

  1. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, 2308, Australia

    Gregorius Satia Budhi & Raymond Chiong

  2. Informatics Department, Petra Christian University, Surabaya, 60236, Indonesia

    Gregorius Satia Budhi

  3. School of Cybersecurity, Chengdu University of Information Technology, Chengdu, 610225, China

    Zuli Wang

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  1. Gregorius Satia Budhi
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Correspondence to Raymond Chiong or Zuli Wang.

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Budhi, G.S., Chiong, R. & Wang, Z. Resampling imbalanced data to detect fake reviews using machine learning classifiers and textual-based features. Multimed Tools Appl 80, 13079–13097 (2021). https://doi.org/10.1007/s11042-020-10299-5

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