| Autor: |
Thi-Kim-Hien Le, Yi-Zhen Li, Sheng-Tun Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 10, Pp 42181-42197 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3167511 |
| Popis: |
Although numerous studies have investigated spam detection and spammer detection on online platforms, they have ignored the fact that reviews written by the same reviewer may be correlated because each reviewer has their own distinct style. The traditional logistic regression model cannot handle this type of data because they violate the independence of residuals assumption. Furthermore, relatively few studies related to fake review detection have considered linguistic and behavioral aspects simultaneously. Thus, we propose a hierarchical logistic regression (HLR)-based model for detecting fake reviews that considers both linguistic and behavioral characteristics. With this outcome, our kernel also has multiple applications, including the detection of review spammers as a pre-module of quality in machine learning. The experimental results demonstrate that HLR can classify fake reviews and review spammers more accurately than the standard machine-learning algorithms. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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