NDLI: A novel method for spammer detection in social networks

Content Provider	IEEE Xplore Digital Library
Author	Xueying Zhang Xianghan Zheng
Copyright Year	2015
Description	Author affiliation: Coll. of Math. & Comput. Sci., Fuzhou Univ., Fuzhou, China (Xueying Zhang; Xianghan Zheng)
Abstract	Online social networks have played an important role in people's common life. Most existing social network platforms, however, face the challenges of dealing with undesirable users and their malicious spam activities that disseminate content, malware, viruses, etc. to the legitimate users of the service. In this paper, an Extreme Learning Machine based supervised machine is proposed for effective spammer detection. The experiment and evaluation show that the proposed solution provides excellent performance with a true positive rate of spammers and non-spammers reaching 99% and 99.95%, respectively. As the results suggest, the proposed solution could achieve better reliability and feasibility compared with existing SVM based approaches.
Starting Page	115
Ending Page	118
File Size	226650
Page Count	4
File Format	PDF
ISBN	9781479977482
e-ISBN	9781479977499
DOI	10.1109/ICSDM.2015.7298036
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines Social network services Extreme learning machine Neural networks Machine learning Feature extraction Social network Data models Mathematical model Spammer
Content Type	Text
Resource Type	Article

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