A fuzzy approach to detect spammer groups

Do, QNT; Hussain, FK; Nguyen, BT

A fuzzy approach to detect spammer groups

Do, QNT Hussain, FK

Nguyen, BT

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2017
Issue Date:: 2017-08-23

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Field	Value	Language
dc.contributor.author	Do, QNT	en_US
dc.contributor.author	Hussain, FK https://orcid.org/0000-0003-1513-8072	en_US
dc.contributor.author	Nguyen, BT	en_US
dc.date.issued	2017-08-23	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2017	en_US
dc.identifier.isbn	9781509060344	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125946
dc.description.abstract	© 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.	en_US
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2017.8015478	en_US
dc.title	A fuzzy approach to detect spammer groups	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 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.

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http://hdl.handle.net/10453/125946