Association for Computing Machinery
Social network spam increases explosively with the rapid development and wide usage of various social networks on the Internet. To timely detect spam in large social network sites, it is desirable to discover unsupervised schemes that can save the training cost of supervised schemes. In this paper, the authors first show several limitations of existing unsupervised detection schemes. The main reason behind the limitations is that existing schemes heavily rely on spamming patterns that are constantly changing to avoid detection. Motivated by their observations, they first propose a sybil defense based spam detection scheme SD2 that remarkably outperforms existing schemes by taking the social network relationship into consideration.