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Advisor(s)
Abstract(s)
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.
Description
This work was partially supported by: (i) Xunta de Galicia grants ED481B-2021-118 and ED481B-2022-093, Spain; and (ii) Portuguese national funds through FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) – as part of project UIDP/50014/2020 (https://doi.org/10.54499/UIDP/50014/2020).
Keywords
Data drift interpretability and explainability Natural Language Processing online Machine Learning spam detection
Citation
de Arriba-Pérez, F., García-Méndez, S., Leal, F., Malheiro, B., & Burguillo, J. C. (2024). Online Detection and Infographic Explanation of Spam Reviews with Data Drift Adaptation. Informatica, 1-25. doi:10.15388/24-INFOR562
Publisher
Institute of Mathematics & Informatics