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SUH-AIFRD: A self-training-based hybrid approach for individual fake reviewer detection

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Abstract

Fake reviews and reviewers pose a significant challenge for online review platforms as they can mislead consumers and harm businesses’ reputations. Traditional methods for detecting fake reviews rely on supervised learning techniques, which require a large amount of labelled data and are not scalable for large datasets. However, detecting fake reviews is challenging due to the uncertainty surrounding reviewer behaviour. Fake reviewers may exhibit similar behaviour patterns to legitimate reviewers, making it difficult to distinguish between them. To address this challenge, this research proposed a novel model, SUH-AIFRD, for detecting fake reviewers in online review platforms. The model uses a hybrid approach of S3SVM, SSDT, and USKMW methods to identify content, behavioural, activity, and relationship features indicative of fake reviews. Behavioural features are utilized to address the problem of reviewer behavioural uncertainty. The SUH-AIFRD model achieved high accuracy of 94.99%, with impressive precision, recall, and F1 score at 94.74%, 95.37%, and 95.05%, respectively. The proposed model has significant implications for businesses, consumers, and online review platforms, providing a more effective way to combat the problem of fake reviews.

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The data and analyses generated during this study are fully available in the published articles [29].

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Motilal Nehru National Institute of Technology Allahabad, Prayagraj, 211004, India

    Arvind Mewada

  2. Bennett University, Greater Noida, Uttar Pradesh, 201310, India

    Arvind Mewada

  3. National Institute of Technical Teachers’ Training and Research, Bhopal, 462002, India

    Rupesh Kumar Dewang

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  1. Arvind Mewada
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  2. Rupesh Kumar Dewang
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All authors contributed equally to this work. Each author was involved in the conceptualization, data collection, analysis and interpretation, and writing of the manuscript. All authors have read and approved the final manuscript and have provided their consent for its publication.

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Correspondence to Arvind Mewada.

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Mewada, A., Dewang, R.K. SUH-AIFRD: A self-training-based hybrid approach for individual fake reviewer detection. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18192-1

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