Above: Pearson correlation and the chi-square ranking according to Weka.

Right: Users correctly classified with features ordered in discrimination power.

The popularity of social bookmarking sites has made them prime targets for spammers.  Many of these systems require an administrator's time and energy to manually filter or remove spam.  Here we discuss the motivations of social spam, and present a study of automatic detection of spammers in a social tagging system. We identify and analyze six distinct features that address various properties of social spam, finding that each of these features provides for a helpful signal to discriminate spammers from legitimate users.  These features are then used in various machine learning algorithms for classification, achieving over 98% accuracy in detecting social spammers with 2% false positives.  These promising results provide a new baseline for future efforts on social spam. We make our dataset publicly available to the research community.