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.

翻译：垃圾评论因其对声誉的显著影响而成为在线平台普遍存在的问题。然而，针对数据流中垃圾评论检测的研究尚显不足。另一关注点在于其透明性需求。为此，本文通过提出一种融合数据漂移适应的在线垃圾评论识别与解释方案来解决这些问题。该方案整合了（i）增量式画像构建，（ii）数据漂移检测与适应，以及（iii）基于机器学习的垃圾评论识别。可解释机制通过仪表板以可视化与文本形式展示预测解释。所获最佳结果达到了87%的垃圾评论F值。