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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. This paper discusses the motivations of social spam, and presents a study of automatic detection of spammers in a social tagging system. Six distinct features are identified and analyzed 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.
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