Constrained NMF-based semi-supervised learning for social media spammer detection

Yu, D; Chen, N; Jiang, F; Fu, B; Qin, A

Constrained NMF-based semi-supervised learning for social media spammer detection

Yu, D Chen, N Jiang, F Fu, B Qin, A

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Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2017, 125 pp. 64 - 73
Issue Date:: 2017-06-01

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Field	Value	Language
dc.contributor.author	Yu, D	en_US
dc.contributor.author	Chen, N	en_US
dc.contributor.author	Jiang, F	en_US
dc.contributor.author	Fu, B	en_US
dc.contributor.author	Qin, A	en_US
dc.date.issued	2017-06-01	en_US
dc.identifier.citation	Knowledge-Based Systems, 2017, 125 pp. 64 - 73	en_US
dc.identifier.issn	0950-7051	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124056
dc.description.abstract	© 2017 Elsevier B.V. Within the past few years, social media platforms such as Facebook, Twitter, and Sina Weibo, have gradually become important channels for information dissemination and communication. However, in the meantime, these platforms are prone to be potentially attacked by spammers, who usually propagate disgusted information such as phishing URLs, false news, and even pornography to other users. Despite rapid increase of social media spammers, the traditional spammer detection methods become less effective. In this paper, we present a novel semi-supervised social media spammer detection approach, making full use of the message content and user behavior as well as the social relation information. First, we adapt the original constrained NMF-based semi-supervised learning (CNMF) algorithm, nonnegative matrix factorization (NMF) by imposing a label information constrain and sparseness constrain. Second, we present a novel CNMF-based integral framework for social media spammer detection by implementing the collaborative factorization on the message content matrix and the user behavior and social relation information matrix. Moreover, we explore the iterative update rule (IUR) and optimization algorithm for the spammer detection model. In addition, its corresponding convergence is also proven. Extensive experiments are conducted on the real-world dataset from Sina Weibo, the experiment results demonstrate that our proposed model performs significantly better than the conventionally applied supervised classifiers for the spammer detection.	en_US
dc.relation.ispartof	Knowledge-Based Systems	en_US
dc.relation.isbasedon	10.1016/j.knosys.2017.03.025	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Constrained NMF-based semi-supervised learning for social media spammer detection	en_US
dc.type	Journal Article
utslib.citation.volume	125	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	1599 Other Commerce, Management, Tourism and Services	en_US
utslib.for	1799 Other Psychology and Cognitive Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
utslib.for	17 Psychology and Cognitive Sciences	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/Provost
pubs.organisational-group	/University of Technology Sydney/Provost/Jumbunna
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	125	en_US

Abstract:

© 2017 Elsevier B.V. Within the past few years, social media platforms such as Facebook, Twitter, and Sina Weibo, have gradually become important channels for information dissemination and communication. However, in the meantime, these platforms are prone to be potentially attacked by spammers, who usually propagate disgusted information such as phishing URLs, false news, and even pornography to other users. Despite rapid increase of social media spammers, the traditional spammer detection methods become less effective. In this paper, we present a novel semi-supervised social media spammer detection approach, making full use of the message content and user behavior as well as the social relation information. First, we adapt the original constrained NMF-based semi-supervised learning (CNMF) algorithm, nonnegative matrix factorization (NMF) by imposing a label information constrain and sparseness constrain. Second, we present a novel CNMF-based integral framework for social media spammer detection by implementing the collaborative factorization on the message content matrix and the user behavior and social relation information matrix. Moreover, we explore the iterative update rule (IUR) and optimization algorithm for the spammer detection model. In addition, its corresponding convergence is also proven. Extensive experiments are conducted on the real-world dataset from Sina Weibo, the experiment results demonstrate that our proposed model performs significantly better than the conventionally applied supervised classifiers for the spammer detection.

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http://hdl.handle.net/10453/124056