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Fake review detecti...
Fake review detection techniques, issues, and future research directions: a literature review
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- Duma, Ramadhani Ally (författare)
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, People’s Republic of China; College of Informatics and Virtual Education, The University of Dodoma, Dodoma, Tanzania
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- Niu, Zhendong (författare)
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
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- Nyamawe, Ally S. (författare)
- College of Informatics and Virtual Education, The University of Dodoma, Dodoma, Tanzania
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- Tchaye-Kondi, Jude (författare)
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
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- Jingili, Nuru (författare)
- Luleå tekniska universitet,Datavetenskap
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- Yusuf, Abdulganiyu Abdu (författare)
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
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- Deve, Augustino Faustino (författare)
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, People’s Republic of China; Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania
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(creator_code:org_t)
- 2024
- 2024
- Engelska.
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Ingår i: Knowledge and Information Systems. - : Springer Nature. - 0219-1377 .- 0219-3116.
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Nyckelord
- Fake review detection
- High- and low-resource languages
- Language code-switching
- Multi-aspect features
- Multilingual
- Reviewer emotions
- Pervasive Mobile Computing
- Distribuerade datorsystem
Publikations- och innehållstyp
- ref (ämneskategori)
- for (ämneskategori)
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