Online detection and infographic explanation of spam reviews with data drift adaptation

• URL: http://arxiv.org/abs/2406.15038v1
• Date: Fri, 21 Jun 2024 10:35:46 GMT
• Title: Online detection and infographic explanation of spam reviews with data drift adaptation
• Authors: Francisco de Arriba-Pérez, Silvia García-Méndez, Fátima Leal, Benedita Malheiro, J. C. Burguillo,
• Abstract summary: This paper proposes 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 best results obtained reached up to 87 % spam F-measure.
• Score: 4.278181795494584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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