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This paper presents a pipeline to detect and explain anomalous reviews inonline platforms. The pipeline is made up of three modules and allows thedetection of reviews that do not generate value for users due to eitherworthless or malicious composition. The classifications are accompanied by anormality score and an explanation that justifies the decision made. Thepipeline's ability to solve the anomaly detection task was evaluated usingdifferent datasets created from a large Amazon database. Additionally, a studycomparing three explainability techniques involving 241 participants wasconducted to assess the explainability module. The study aimed to measure theimpact of explanations on the respondents' ability to reproduce theclassification model and their perceived usefulness. This work can be useful toautomate tasks in review online platforms, such as those for electroniccommerce, and offers inspiration for addressing similar problems in the fieldof anomaly detection in textual data. We also consider it interesting to havecarried out a human evaluation of the capacity of different explainabilitytechniques in a real and infrequent scenario such as the detection of anomalousreviews, as well as to reflect on whether it is possible to explain tasks ashumanly subjective as this one.

Explained anomaly detection in text reviews: Can subjective scenarios be correctly evaluated?