Fake review identification and utility evaluation model using machine learning
Due to the structural growth of e-commerce platforms, the frequency of exchange of opinions and the number of online reviews of platform participants related to products are increasing. However, given the growth of fake reviews, the corresponding growth in the quality of online reviews seems to be s...
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| Published in | Frontiers in artificial intelligence Vol. 5; p. 1064371 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
Frontiers Media S.A
19.01.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2624-8212 2624-8212 |
| DOI | 10.3389/frai.2022.1064371 |
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| Abstract | Due to the structural growth of e-commerce platforms, the frequency of exchange of opinions and the number of online reviews of platform participants related to products are increasing. However, given the growth of fake reviews, the corresponding growth in the quality of online reviews seems to be slow, at best. The number of cases of harm to retailers and customers caused by malicious false reviews is steadily increasing every year. In this context, it is becoming difficult for users to determine useful reviews amid a flood of information. As a result, the intrinsic value of online reviews that reduce uncertainty in pre-purchase decisions is blurred, and e-commerce platforms are on the verge of losing credibility and traffic. Through this study, we intend to present solutions related to review filtering and classification by constructing a model for judging the authenticity and usefulness of online reviews using machine learning. |
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| AbstractList | Due to the structural growth of e-commerce platforms, the frequency of exchange of opinions and the number of online reviews of platform participants related to products are increasing. However, given the growth of fake reviews, the corresponding growth in the quality of online reviews seems to be slow, at best. The number of cases of harm to retailers and customers caused by malicious false reviews is steadily increasing every year. In this context, it is becoming difficult for users to determine useful reviews amid a flood of information. As a result, the intrinsic value of online reviews that reduce uncertainty in pre-purchase decisions is blurred, and e-commerce platforms are on the verge of losing credibility and traffic. Through this study, we intend to present solutions related to review filtering and classification by constructing a model for judging the authenticity and usefulness of online reviews using machine learning. Due to the structural growth of e-commerce platforms, the frequency of exchange of opinions and the number of online reviews of platform participants related to products are increasing. However, given the growth of fake reviews, the corresponding growth in the quality of online reviews seems to be slow, at best. The number of cases of harm to retailers and customers caused by malicious false reviews is steadily increasing every year. In this context, it is becoming difficult for users to determine useful reviews amid a flood of information. As a result, the intrinsic value of online reviews that reduce uncertainty in pre-purchase decisions is blurred, and e-commerce platforms are on the verge of losing credibility and traffic. Through this study, we intend to present solutions related to review filtering and classification by constructing a model for judging the authenticity and usefulness of online reviews using machine learning.Due to the structural growth of e-commerce platforms, the frequency of exchange of opinions and the number of online reviews of platform participants related to products are increasing. However, given the growth of fake reviews, the corresponding growth in the quality of online reviews seems to be slow, at best. The number of cases of harm to retailers and customers caused by malicious false reviews is steadily increasing every year. In this context, it is becoming difficult for users to determine useful reviews amid a flood of information. As a result, the intrinsic value of online reviews that reduce uncertainty in pre-purchase decisions is blurred, and e-commerce platforms are on the verge of losing credibility and traffic. Through this study, we intend to present solutions related to review filtering and classification by constructing a model for judging the authenticity and usefulness of online reviews using machine learning. |
| Author | Nam, Kyungmin Hwang, Sangyoon Choi, Wonil Yang, Seoyi Park, Minwoo Oh, Hayoung |
| AuthorAffiliation | 2 College of Computing and Informatics, Sungkyunkwan University , Seoul , South Korea 1 Department of Business Administration, Sungkyunkwan University , Seoul , South Korea |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36744111$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.3390/su7044668 10.1016/j.ipm.2019.03.002 10.38115/asgba.2020.17.6.219 10.14329/isr.2021.23.1.045 |
| ContentType | Journal Article |
| Copyright | Copyright © 2023 Choi, Nam, Park, Yang, Hwang and Oh. Copyright © 2023 Choi, Nam, Park, Yang, Hwang and Oh. 2023 Choi, Nam, Park, Yang, Hwang and Oh |
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| Keywords | SVC useful reviews fake review e-commerce fake review detection technique logistic regression machine learning |
| Language | English |
| License | Copyright © 2023 Choi, Nam, Park, Yang, Hwang and Oh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Ricky J. Sethi, Fitchburg State University, United States This article was submitted to Machine Learning and Artificial Intelligence, a section of the journal Frontiers in Artificial Intelligence Reviewed by: Jie Zhang, Nanyang Technological University, Singapore; Giseop Noh, Cheongju University, South Korea |
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| References | Hyun (B4) 2019; 25 Park (B12) 2021; 6 B13 Yao (B15) 2020; 29 B14 Lee (B9) 2020 Lee (B8) 2021; 23 B2 Lee (B10) 2014; 7 Jung (B7) 2020; 17 B5 B6 Chengzhang (B3) 2014; 1 Barbado (B1) 2019; 56 Myung (B11) 2008; 35 |
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| SubjectTerms | Artificial Intelligence e-commerce fake review fake review detection technique machine learning SVC useful reviews |
| Title | Fake review identification and utility evaluation model using machine learning |
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