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.

本文提出了一个用于在在线平台上检测和解释异常评论的流程。该流程由三个模块组成，可检测出无价值或恶意组合的评论，并提供分类的正常度评分和解释。通过对大型亚马逊数据库创建的不同数据集进行评估，证明了该流程解决异常检测任务的能力。此外，通过对 241 名参与者进行三种可解释性技术的比较研究，评估了可解释性模块的影响。该研究旨在衡量解释对被调查人重现分类模型以及其感知有用性的影响。这项工作对于自动化处理在线评论平台（如电子商务）中的任务，并为解决文本数据异常检测领域的类似问题提供了灵感。我们还认为，在检测异常评论等真实且罕见的场景中，进行了对不同解释技术能力的人类评估，这一点很有意义，同时也需要对是否可能解释这类主观任务进行思考。