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Fake review detection techniques, issues, and future research directions: a literature review

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Abstract

Recently, the impact of product or service reviews on customers' purchasing decisions has become increasingly significant in online businesses. Consequently, manipulating reviews for fame or profit has become prevalent, with some businesses resorting to paying fake reviewers to post spam reviews. Given the importance of reviews in decision-making, detecting fake reviews is crucial to ensure fair competition and sustainable e-business practices. Although significant efforts have been made in the last decade to distinguish credible reviews from fake ones, it remains challenging. Our literature review has identified several gaps in the existing research: (1) most fake review detection techniques have been proposed for high-resource languages such as English and Chinese, and few studies have investigated low-resource and multilingual fake review detection, (2) there is a lack of research on deceptive review detection for reviews based on language code-switching (code-mix), (3) current multi-feature integration techniques extract review representations independently, ignoring correlations between them, and (4) there is a lack of a consolidated model that can mutually learn from review emotion, coarse-grained (overall rating), and fine-grained (aspect ratings) features to supplement the problem of sentiment and overall rating inconsistency. In light of these gaps, this study aims to provide an in-depth literature analysis describing strengths and weaknesses, open issues, and future research directions.

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Notes

  1. https://www.scopus.com/standard/marketing.uri#basic.

  2. https://www.webofscience.com/wos/woscc/basic-search.

  3. https://link.springer.com/.

  4. https://www.acm.org/.

  5. https://www.ieee.org/.

  6. 6 https://www.sciencedirect.com/.

  7. https://myleott.com/opspam.html.

  8. https://myleott.com/opspam.html.

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Funding

This work was supported by the National Natural Science Foundation of China under Grant 62272048.

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Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, People’s Republic of China

    Ramadhani Ally Duma, Zhendong Niu, Jude Tchaye-Kondi, Abdulganiyu Abdu Yusuf & Augustino Faustino Deve

  2. College of Informatics and Virtual Education, The University of Dodoma, Dodoma, Tanzania

    Ramadhani Ally Duma & Ally S. Nyamawe

  3. Luleå University of Technology, Skellefteå, Sweden

    Nuru Jingili

  4. Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania

    Augustino Faustino Deve

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  1. Ramadhani Ally Duma
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  4. Jude Tchaye-Kondi
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Contributions

Ramadhani Duma was involved in the conceptualization, formal analysis, investigation, resources, and writing—original draft. Zhendong Niu and Ally Nyamawe contributed to the methodology, resources, supervision, validation, editing and review, and project administration. Jude Tchaye-Kondi, Abdulganiyu Yusuf, Nuru Jingili, and Augustino Deve performed the editing and review

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Correspondence to Zhendong Niu.

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Duma, R.A., Niu, Z., Nyamawe, A.S. et al. Fake review detection techniques, issues, and future research directions: a literature review. Knowl Inf Syst 66, 5071–5112 (2024). https://doi.org/10.1007/s10115-024-02118-2

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