Detecting spammers using review graph
by Chonglin Gu; Zhixiang He; Shi Chen; Hejiao Huang; Xiaohua Jia
International Journal of High Performance Computing and Networking (IJHPCN), Vol. 10, No. 4/5, 2017

Abstract: In recent years, e-commerce is so popular that many consumers make transactions online. In order to make more profit, some merchants hire spammers to give high ratings to promote certain products, or to give malicious negative reviews to defame products of competitors. Those misleading reviews are destructive to the fairness of e-commerce environment. Therefore, it is very important to detect spammers who are always posting deceptive reviews. However, existing methods have low recognition rate for detecting spam reviews. In this paper, we first propose to use SCTD to reduce the whole dataset, so that we can focus on the periods when spammers are more likely to happen. And then, a similarity graph is built to describe the relationships between those reviewers who post reviews on the same products. Finally, we propose an iterative algorithm to calculate the spam score for each reviewer using the edge weight and key features of adjacent reviewers in the graph. Experiment results show that our proposed method is much more effective in spammers detection.

Online publication date: Tue, 12-Sep-2017

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of High Performance Computing and Networking (IJHPCN):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com