Book:
Lecture Notes in Networks and Systems
(Proceedings of International Conference on Fourth Industrial Revolution and Beyond 2021)
Volume 437
(2022),
p. 357
2022 5th International Conference on Information and Communications Technology (ICOIACT)
Anthony Chandra, Gregorius, M. S. John Immanuel, Alexander Agung Santoso Gunawan, Anderies
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.