Evidential Spammers and Group Spammers Detection

Malika Ben Khalifa (1) , Zied Elouedi (1) , Eric Lefevre (2)

1 LARODEC - Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus
2 LGI2A - Laboratoire de Génie Informatique et d'Automatique de l'Artois

The online success of the brands, products or services depends upon the online reviews written by the consumers to share their experiences. These reviews deeply affect the buying decision of the new customers. For the purpose of performing their e-reputation, some companies rely on spammers to involve fraud reviews with the aim of gaining more profit. They can work individually or collaborate together to post various fake reviews trying to promote or demote target companies or product. These spammers and the group of spammers mislead the readers which make the e-commerce unsafe domain. To deal with this issue, we propose a new method having the objective to detect the spammers while taking into account both the group spammers and the individual spammers indicators. Our proposed method relies on the K-nearest neighbors algorithm under the belief function theory in order to handle the uncertainty in both the spammers and the group spammers indicators. Experiments are conducted on two labeled real datasets extracted from Yelp.com where our method achieves significant results.

Mots clés: Fake reviews; Spammers; Group spammers; Uncertainty; Belief Function Theory; EK-NN; E-commerce

Domaines: Intelligence artificielle [cs.AI]

Malika Ben Khalifa, Zied Elouedi, Eric Lefevre. Evidential Spammers and Group Spammers Detection. ISDA'2021, Dec 2021, New-York, United States. pp.255-265, ⟨10.1007/978-3-030-96308-8_23⟩. ⟨hal-03643790⟩