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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Acknowledgments
This work is supported by the Basic and Advanced Research Projects in Chongqing under Grant No. cstc2015jcyjA40049, the National Key Basic Research Program of China (973) under Grant No. 2013CB328903, the National Natural Science Foundation of China under Grant Nos. 61472021 and 61602070, the Fundamental Research Fund for the Central Universities under Grant No. 106112014CDJZR095502, and the China Scholarship Council.
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Li, W., Gao, M., Rong, W., Wen, J., Xiong, Q., Ling, B. (2016). LSSL-SSD: Social Spammer Detection with Laplacian Score and Semi-supervised Learning. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_35
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DOI: https://doi.org/10.1007/978-3-319-47650-6_35
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