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

Malika BEN KHALIFA

Ph.D. student, ATER
(Left the LGI2A in 2022)
Member of the research themes:

Conférence Internationale avec Comité de Lecture

2021
International conference with review committee
DOI
Malika BEN KHALIFA -- Zied ELOUEDI -- Eric LEFEVRE
Evidential Spammers and Group Spammers Detection
21th International Conference on Intelligent Systems Design and Applications, ISDA 2021, pp 255-265, December 13-15 2021, 12/2021
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, 06/2020
2020
International conference with review committee
Malika BEN KHALIFA -- Zied ELOUEDI -- Eric LEFEVRE
An evidential spammer detection based on the suspicious behaviors’ indicators
International multi-conference on: Organization of knowledge an advances technologies, OCTA'2020, pp 1-8, 02/2020
2019
International conference with review committee
Malika BEN KHALIFA -- Zied ELOUEDI -- Eric LEFEVRE
Multiple criteria fake reviews detection based on spammers’ indicators within the belief function theory
International Conference on Hybrid Intelligent Systems, HIS'2019, No. 145-155, 12/2019
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, 11/2019
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, 07/2019
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, 12/2018
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, 09/2018

Conférence Nationale avec Comité de Lecture

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, 11/2018

Author of the Ph.D. thesis "Dealing with uncertainty in fake reviews detection within the belief function theory"

2019 - 2022

Online success of the brands, products, or services is dependent on the online reviews written by the costumers who share their experiences. These reviews become an essential factor in customers’ purchasing decision. Driven by the immense financial profits, some corrupt individuals or organizations deliberately post fake reviews to promote their products or to demote their competitors’ products, trying to mislead or influence customers. Therefore, it is crucial to spot fake reviews in order to maintain the credibility of online reviews. Dealing with the uncertain aspect is essential, since these reviews are issued from the human opinions which are generally uncertain and full of ambiguity due to a large number of the included fraudulent ones. Thus, this thesis proposes to handle the fake reviews detection under uncertainty. Such uncertainty is represented and managed through the belief function framework and through some evidential machine learning techniques. We propose new evidential approaches for the detection of fake reviews based on three aspects: reviews, spammers and spammer groups. Moreover, a hybrid method combining both the spammers and the group spammers categories is also performed leading to a better detection quality. To substantiate the efficiency and the impact of our proposals compared to the literature, experiments are conducted on two labelled real-world review data-sets extracted from Yelp.com.