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ELM-based spammer detection in social networks

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Abstract

Online social networks, such as Facebook, Twitter, and Weibo 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. The spreading of spam degrades user experience and also negatively impacts server-side functions such as data mining, user behavior analysis, and resource recommendation. In this paper, an extreme learning machine (ELM)-based supervised machine is proposed for effective spammer detection. The work first constructs the labeled dataset through crawling Sina Weibo data and manually classifying corresponding users into spammer and non-spammer categories. A set of features is then extracted from message content and user behavior and applies them to the ELM-based spammer classification algorithm. 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.

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Acknowledgments

This paper is supported by the National Natural Science Foundation of China under Grant No. 61103175 and No.11271002, the Key Project of Chinese Ministry of Education under Grant No.212086; the Technology Innovation Platform Project of Fujian Province under Grant No. 2009J1007, No. 2013H6011 and 2013J01228; the Key Project Development Foundation of Education Committee of Fujian province under Grand No. JA11011 and JA12016.

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Authors and Affiliations

  1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350108, China

    Xianghan Zheng, Xueying Zhang & Yuanlong Yu

  2. Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou, 350108, China

    Xianghan Zheng, Xueying Zhang & Yuanlong Yu

  3. School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland

    Tahar Kechadi

  4. Department of Electrical Engineering and Computer Science, University of Stavanger, 4036, Stavanger, Norway

    Chunming Rong

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  1. Xianghan Zheng
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  2. Xueying Zhang
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Corresponding author

Correspondence to Yuanlong Yu.

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Zheng, X., Zhang, X., Yu, Y. et al. ELM-based spammer detection in social networks. J Supercomput 72, 2991–3005 (2016). https://doi.org/10.1007/s11227-015-1437-5

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  • DOI: https://doi.org/10.1007/s11227-015-1437-5

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