A photo shows the logo of Renren.com. Will robots take over our world? These questions, which once seemed irrelevant, now frequently come into our minds with the advancement of Artificial Intelligence (AI). A recent report shows that there are almost no active users left on Renren, as advertising accounts keep pushing uninteresting contents and the system keeps recommending other people's posts that were so "yesterday". Some have jokingly said this must be what will happen to our world after it is taken by AI.

Renren.com, once a leading Chinese online social network, is now dubbed as a "cyber Chernobyl". A recent report shows that there are almost no active users left on Renren, as advertising accounts keep pushing uninteresting contents and the system keeps recommending other people's posts that were so "yesterday". Some have jokingly said this must be what will happen to our world after it is taken by AI. Although it is still hard to say whether AI will ultimately do us good or pose threats, robots are indeed gradually entering our lives.

As the popularity of the social media increases, as evidenced in Twitter, Facebook and China's Renren, spamming activities also picked up in numbers and variety. On social network sites, spammers often disguise themselves by creating fake accounts and hijacking normal users' accounts for personal gains. Different from the spammers in traditional systems such as SMS and email, spammers in social media behave like normal users and they continue to change their spamming strategies to fool anti spamming systems. However, due to the privacy and resource concerns, many social media websites cannot fully monitor all the contents of users, making many of the previous approaches, such as topology-based and content-classification-based methods, infeasible to use. In this paper, we propose a novel method for spammer detection in social networks that exploits both social activities as well as users' social relations in an innovative and highly scalable manner. The proposed method detects spammers following collective activities based on users' social actions and relations. We have empirically tested our method on data from Renren.com, which is the largest social network in China, and demonstrated that our new method can improve the detection performance significantly.