International Journal of Computer Applications
Increase in number of spam incidents is causing a very serious threat to social networking world which has in turn become an important means of interaction and communication between public users. It is not only dangerous to the public users, but it also covers much of the bandwidth of the Internet traffic. Most of current spam filters in use are based on the subject content of E-mail, Facebook and Twitter. Social networking services also provide great possibilities to take advantage of user identification and other social graph-dependent features to improve classification. In this paper, the proposed system uses machine learning approach for spam detection based on features extracted from social networks constructed from social networking site message metadata and logs.