  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.
