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

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dc.contributor.authorDuma, Ramadhani Ally
dc.contributor.authorNiu, Zhendong
dc.contributor.authorNyamawe, Ally S.
dc.contributor.authorTchaye-Kondi, Jude
dc.contributor.authorJingili, Nuru
dc.contributor.authorYusuf, Abdulganiyu Abdu
dc.contributor.authorDeve, Augustino Faustino
dc.date.accessioned2024-09-09T10:53:20Z
dc.date.available2024-09-09T10:53:20Z
dc.date.issued2024
dc.descriptionAbstract. Full text available at https://doi.org/10.1007/s10115-024-02118-2
dc.description.abstractRecently, 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.
dc.identifier.citationDuma, R. A., Niu, Z., Nyamawe, A. S., Tchaye-Kondi, J., Jingili, N., Yusuf, A. A., & Deve, A. F. (2024). Fake review detection techniques, issues, and future research directions: a literature review. Knowledge and Information Systems, 1-42.
dc.identifier.doi10.1007/s10115-024-02118-2
dc.identifier.otherDOI:10.1007/s10115-024-02118-2
dc.identifier.urihttps://repository.udom.ac.tz/handle/20.500.12661/4954
dc.language.isoen
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofKnowledge and Information Systems
dc.subjectfake reviewers
dc.subjectfair competition
dc.subjectmultilingual
dc.subjectlanguage code-switching
dc.titleFake review detection techniques, issues, and future research directions: a literature review
dc.typejournal-article
oaire.citation.issue9
oaire.citation.volume66
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