Evidential Spammers and Group Spammers Detection

Abstract

AbstractThe 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 products. 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.KeywordsFake reviewsSpammersGroup spammersUncertaintyBelief Function TheoryEK-NNE-commerce