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Anomaly Detection in Online User Review

EasyChair Preprint no. 12924

31 pagesDate: April 6, 2024


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.

Keyphrases: anomaly detection, Explainability, Text Reviews, transformers

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ananthula Jeevan Kumar and Attipamula Anand Netha and Cherukuri Jaswanth Santosh Kumar and Bandi Pavan Kalyan Reddy and Ankireddypalle Shashank Kumar Reddy},
  title = {Anomaly Detection in Online User Review},
  howpublished = {EasyChair Preprint no. 12924},

  year = {EasyChair, 2024}}
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