A novel method for spammer detection in social networks

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.

[1]  Yoshua Bengio.  Scaling up deep learning , 2014, KDD.

[2]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[3]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[4]  Yamashita,et al.  Backpropagation algorithm which varies the number of hidden units , 1989 .

[5]  K. S. Banerjee.  Generalized Inverse of Matrices and Its Applications , 1973 .

[6]  Xiaolong Wang,et al.  Active deep learning method for semi-supervised sentiment classification , 2013, Neurocomputing.

[7]  Haiying Shen,et al.  Leveraging Social Networks for Effective Spam Filtering , 2014, IEEE Transactions on Computers.

[8]  Muhammad Abulaish,et al.  Community-based features for identifying spammers in Online Social Networks , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[9]  Zexuan Zhu,et al.  A fast pruned-extreme learning machine for classification problem , 2008, Neurocomputing.

[10]  Alan J. Mayne,et al.  Generalized Inverse of Matrices and its Applications , 1972 .

[11]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[12]  Vern Paxson,et al.  @spam: the underground on 140 characters or less , 2010, CCS '10.

[13]  Geoffrey E. Hinton.  Learning multiple layers of representation , 2007, Trends in Cognitive Sciences.

[14]  Zheyi Chen,et al.  Mobile Cloud Based Framework for Remote-Resident Multimedia Discovery and Access , 2014 .

[15]  Bin Wu,et al.  SDHM: A hybrid model for spammer detection in Weibo , 2014, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014).