Sammendrag
Abstract Online social networks, such as Facebook, Twitter, andWeibo 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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