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2016 | OriginalPaper | Buchkapitel

LSSL-SSD: Social Spammer Detection with Laplacian Score and Semi-supervised Learning

verfasst von : Wentao Li, Min Gao, Wenge Rong, Junhao Wen, Qingyu Xiong, Bin Ling

Erschienen in: Knowledge Science, Engineering and Management

Verlag: Springer International Publishing

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Abstract

The rapid development of social networks makes it easy for people to communicate online. However, social networks usually suffer from social spammers due to their openness. Spammers deliver information for economic purposes, and they pose threats to the security of social networks. To maintain the long-term running of online social networks, many detection methods are proposed. But current methods normally use high dimension features with supervised learning algorithms to find spammers, resulting in low detection performance. To solve this problem, in this paper, we first apply the Laplacian score method, which is an unsupervised feature selection method, to obtain useful features. Based on the selected features, the semi-supervised ensemble learning is then used to train the detection model. Experimental results on the Twitter dataset show the efficiency of our approach after feature selection. Moreover, the proposed method remains high detection performance in the face of limited labeled data.

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Titel: LSSL-SSD: Social Spammer Detection with Laplacian Score and Semi-supervised Learning
verfasst von: Wentao Li
Min Gao
Wenge Rong
Junhao Wen
Qingyu Xiong
Bin Ling
Verlag: Springer International Publishing
Buch: Knowledge Science, Engineering and Management
Print ISBN: 978-3-319-47649-0

Electronic ISBN: 978-3-319-47650-6

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-47650-6_35

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