TY - JOUR
T1 - Fake review detection techniques, issues, and future research directions
T2 - a literature review
AU - Duma, Ramadhani Ally
AU - Niu, Zhendong
AU - Nyamawe, Ally S.
AU - Tchaye-Kondi, Jude
AU - Jingili, Nuru
AU - Yusuf, Abdulganiyu Abdu
AU - Deve, Augustino Faustino
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
PY - 2024/9
Y1 - 2024/9
N2 - 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.
AB - 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.
KW - Fake review detection
KW - High- and low-resource languages
KW - Language code-switching
KW - Multi-aspect features
KW - Multilingual
KW - Reviewer emotions
UR - http://www.scopus.com/inward/record.url?scp=85193271928&partnerID=8YFLogxK
U2 - 10.1007/s10115-024-02118-2
DO - 10.1007/s10115-024-02118-2
M3 - Review article
AN - SCOPUS:85193271928
SN - 0219-1377
VL - 66
SP - 5071
EP - 5112
JO - Knowledge and Information Systems
JF - Knowledge and Information Systems
IS - 9
ER -