Laboratoire de Génie Informatique et d’Automatique de l’Artois

Malika BEN KHALIFA

Ph.D. student
Member of the research themes:
Contact information:
2020
International conference with review committee
Malika BEN KHALIFA -- Zied ELOUEDI -- Eric LEFEVRE
An evidential group spammers detection
8th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU'2020, pp 1-14, juin 2020
2019
International conference with review committee
Malika BEN KHALIFA -- Zied ELOUEDI -- Eric LEFEVRE
Fake reviews detection based on both the review and the reviewer features under belief function theory
16th international conference Applied Computing, AC'2019, pp 123-130, novembre 2019
2019
International conference with review committee
Malika BEN KHALIFA -- Zied ELOUEDI -- Eric LEFEVRE
Spammers detection based on reviewers’ behaviors under belief function theory
32th International Conference on Industrial, Engineering other Applications of Applied Intelligent Systems, IEA-AIE'2019, juillet 2019
2018
International conference with review committee
Malika BEN KHALIFA -- Zied ELOUEDI -- Eric LEFEVRE
Multiple criteria fake reviews detection using belief function theory
18th International Conference on Intelligent Systems Design and Applications, ISDA 2018, décembre 2018
2018
French conference with review committee
Malika BEN KHALIFA -- Zied ELOUEDI -- Eric LEFEVRE
Détection des faux avis dans un cadre évidentiel
27e Rencontres Francophones sur la Logique Floue et ses Applications, LFA 2018, pp 135-142, novembre 2018
2018
International conference with review committee
Malika BEN KHALIFA -- Zied ELOUEDI -- Eric LEFEVRE
Fake Reviews Detection Under Belief Function Framework
4th International Conference on Advanced Intelligent Systems and Informatics, AISI 2018, pp 395-404, septembre 2018

Ph.D. topic: "Dealing with uncertainty in fake reviews detection within the belief function theory"

2019

The online reviews play an increasingly spreading role in consumer purchasing decisions and they are also considered as one of the most powerful source of information for companies. Due to this attraction, companies rely on spammers to promote their own products and demote the competitors one by posting fake reviews. Therefore, it is essential to detect deceptive reviews in order to ensure customers con-fidence and to maintain companies’ fair competition.
To tackle this problem, we propose an approach able to spot spam reviews relying both on the rating reviews and the different spammers indicators under belief function framework.