  This paper presents a pipeline to detect and explain anomalous reviews in
online platforms. The pipeline is made up of three modules and allows the
detection of reviews that do not generate value for users due to either
worthless or malicious composition. The classifications are accompanied by a
normality score and an explanation that justifies the decision made. The
pipeline's ability to solve the anomaly detection task was evaluated using
different datasets created from a large Amazon database. Additionally, a study
comparing three explainability techniques involving 241 participants was
conducted to assess the explainability module. The study aimed to measure the
impact of explanations on the respondents' ability to reproduce the
classification model and their perceived usefulness. This work can be useful to
automate tasks in review online platforms, such as those for electronic
commerce, and offers inspiration for addressing similar problems in the field
of anomaly detection in textual data. We also consider it interesting to have
carried out a human evaluation of the capacity of different explainability
techniques in a real and infrequent scenario such as the detection of anomalous
reviews, as well as to reflect on whether it is possible to explain tasks as
humanly subjective as this one.
