Online detection and infographic explanation of spam reviews with data drift adaptation
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909228870926336 |
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| author | de Arriba-Pérez, Francisco García-Méndez, Silvia Leal, Fátima Malheiro, Benedita Burguillo, J. C. |
| author_facet | de Arriba-Pérez, Francisco García-Méndez, Silvia Leal, Fátima Malheiro, Benedita Burguillo, J. C. |
| contents | Spam reviews are a pervasive problem on online platforms due to its significant impact on reputation. However, research into spam detection in data streams is scarce. Another concern lies in their need for transparency. Consequently, this paper addresses those problems by proposing an online solution for identifying and explaining spam reviews, incorporating data drift adaptation. It integrates (i) incremental profiling, (ii) data drift detection & adaptation, and (iii) identification of spam reviews employing Machine Learning. The explainable mechanism displays a visual and textual prediction explanation in a dashboard. The best results obtained reached up to 87 % spam F-measure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15038 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Online detection and infographic explanation of spam reviews with data drift adaptation de Arriba-Pérez, Francisco García-Méndez, Silvia Leal, Fátima Malheiro, Benedita Burguillo, J. C. Machine Learning Artificial Intelligence Computation and Language Social and Information Networks Spam reviews are a pervasive problem on online platforms due to its significant impact on reputation. However, research into spam detection in data streams is scarce. Another concern lies in their need for transparency. Consequently, this paper addresses those problems by proposing an online solution for identifying and explaining spam reviews, incorporating data drift adaptation. It integrates (i) incremental profiling, (ii) data drift detection & adaptation, and (iii) identification of spam reviews employing Machine Learning. The explainable mechanism displays a visual and textual prediction explanation in a dashboard. The best results obtained reached up to 87 % spam F-measure. |
| title | Online detection and infographic explanation of spam reviews with data drift adaptation |
| topic | Machine Learning Artificial Intelligence Computation and Language Social and Information Networks |
| url | https://arxiv.org/abs/2406.15038 |