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Multiple Criteria Fake Reviews Detection Using Belief Function Theory

  • Malika Ben KhalifaEmail author
  • Zied Elouedi
  • Eric Lefèvre
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

Checking online reviews before making a purchase becomes a permanent habit. Hence, online consumer reviews, product and services play an increasingly spreading role in consumer purchasing decisions. Unfortunately, the importance of advertising and the attraction of profit have led to the appearance of fake reviews in order to mislead readers. Considering that the reviews are generally imperfect, the spam reviews detection becomes one of the most important problems. To tackle this problem, we propose a new method of multi-criteria fake reviews under belief function theory. This approach treats the uncertainty in the rating reviewers’ given to multiple evaluation criteria, takes into account the similarity between all provided reviews and deals with missing data. We evaluate our method through artificial datasets. Then, we use a real dataset to validate it. The results prove that the proposed approach is a useful solution for the fake reviews detection problem.

Keywords

Online reviews Multi-criteria evaluation Fake reviews Uncertainty Belief function theory 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Malika Ben Khalifa
    • 1
    Email author
  • Zied Elouedi
    • 1
  • Eric Lefèvre
    • 2
  1. 1.Université de Tunis, Institut Supérieur de Gestion de Tunis, LARODECTunisTunisia
  2. 2.Univ. Artois, EA 3926, Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A)BéthuneFrance

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