The popularity of social bookmarking systems became attractive to spammers to disturb systems by posting illegal or inappropriate web content links that users do not wish to share. We present a study of automatic detection of spammers in a social tagging system. Several distinct features are extracted that address various properties of social spam, which provide sufficient information to discriminate legitimate against spammer users. So these features are used for various machine learning algorithms to classify, achieving over 99% accuracy in detecting spammers.
Spam detection in social bookmarking websites - IEEE Conference Publication