A fuzzy approach to detect spammer groups

Publication Type:
Conference Proceeding
Citation:
IEEE International Conference on Fuzzy Systems, 2017
Issue Date:
2017-08-23
Full metadata record
Files in This Item:
Filename Description Size
08015478.pdfPublished version760.76 kB
Adobe PDF
© 2017 IEEE. Cloud computing has been advancing at an impressive rate in recent years and is likely to increase more and more in the near future. New services are being developed constantly, such as cloud infrastructure, security and platform as a service, to name just a few. Due to the vast pool of available services, review websites have been created to help customers make decisions for their business. This leads to some reviewers taking advantage of these tools to promote the providers that hire them or to discredit competitors. These reviewers can either act individually or cooperate with each other. When reviewers collude to promote one product or defame another, they are called spammer groups. In this paper, we present an approach to identify spammer groups. First, a network-based method is used to identify individual spam reviewers. Then, a fuzzy k-means clustering algorithm is used to find the group that they belong to. A case study that suggests which group an incorrect review belongs to is provided to further understand the new method.
Please use this identifier to cite or link to this item: