| Autor: |
Syed Abdullah Ashraf, Aariz Faizan Javed, Sreevatsa Bellary, Pradip Kumar Bala, Prabin Kumar Panigrahi |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Journal of Theoretical and Applied Electronic Commerce Research, Vol 19, Iss 2, Pp 1517-1558 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
0718-1876 |
| DOI: |
10.3390/jtaer19020075 |
| Popis: |
Driven by motives of profit and competition, fake reviews are increasingly used to manipulate product ratings. This trend has caught the attention of academic researchers and international regulatory bodies. Current methods for spotting fake reviews suffer from scalability and interpretability issues. This study focuses on identifying suspected fake reviews in the hospitality sector using a review aggregator platform. By combining features and leveraging various classifiers through a stacking architecture, we improve training outcomes. User-centric traits emerge as crucial in spotting fake reviews. Incorporating SHAP (Shapley Additive Explanations) enhances model interpretability. Our model consistently outperforms existing methods across diverse dataset sizes, proving its adaptable, explainable, and scalable nature. These findings hold implications for review platforms, decision-makers, and users, promoting transparency and reliability in reviews and decisions. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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