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Social networks are growing at an alarming rate. With networks like Facebook and Twitter attracting audiences of over 100 million users a month, spammers too are attracted to such networks to propagate spam. Spam is a nuisance to people and hinders them from consuming the information they need or want to find on social networks. Even with individual social networks capable of filtering a small subset of spam particular to their social network, there is a lot of spam that remains unfiltered on these networks. Instead of each social network building their own advanced detection mechanism or having to identify new spam, we propose a social-spam classification framework which can be used by the various social networks using general profile, message and webpage models. Using this framework, a new type of spam detected on one network, could automatically identified on other networks and new social networking sites could plug-in easily to protect themselves against spam.
