Detecting spammers on social networks
Social network has become a very popular way for internet users to communicate and interact online. Users spend plenty of time on famous social networks (e.g. Facebook, Twitter, Sina Weibo, etc. reading news, discussing events and posting messages. Unfortunately, this popularity also attracts a significant amount of spammers who continuously expose malicious behavior (e.g. post messages containing commercial URLs, following a larger amount of users, etc. leading to great misunderstanding and inconvenience on users? social activities. In this paper, a supervised machine learning based solution is proposed for an effective spammer detection. The main procedure of the work is: first, collect a dataset from Sina Weibo including 30,116 users and more than 16 million messages. Then, construct a labeled dataset of users and manually classify users into spammers and non spammers. Afterwards, extract a set of feature from message content and users? social behavior, and apply into SVM (Support Vector Machines) based spammer detection algorithm. The experiment shows that the proposed solution is capable to provide excellent performance with true positive rate of spammers and non spammers reaching 99.1% and 99.9% respectively.
Neurocomputing