J Supercomput DOI 10.1007/s11227-015-1437-5 ELM-based spammer detection in social networks Xianghan Zheng 1,2 · Xueying Zhang 1,2 · Yuanlong Yu 1,2 · Tahar Kechadi 3 · Chunming Rong 4 © Springer Science+Business Media New York 2015 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 corre- sponding 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 pro- posed 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 exist- ing SVM-based approaches. B Yuanlong Yu yu.yuanlong@fzu.edu.cn 1 College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China 2 Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou 350108, China 3 School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland 4 Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway 123