  In the last decade we have witnessed the explosive growth of online social
networking services (SNSs) such as Facebook, Twitter, RenRen and LinkedIn.
While SNSs provide diverse benefits for example, forstering interpersonal
relationships, community formations and news propagation, they also attracted
uninvited nuiance. Spammers abuse SNSs as vehicles to spread spams rapidly and
widely. Spams, unsolicited or inappropriate messages, significantly impair the
credibility and reliability of services. Therefore, detecting spammers has
become an urgent and critical issue in SNSs. This paper deals with Follow spam
in Twitter. Instead of spreading annoying messages to the public, a spammer
follows (subscribes to) legitimate users, and followed a legitimate user. Based
on the assumption that the online relationships of spammers are different from
those of legitimate users, we proposed classification schemes that detect
follow spammers. Particularly, we focused on cascaded social relations and
devised two schemes, TSP-Filtering and SS-Filtering, each of which utilizes
Triad Significance Profile (TSP) and Social status (SS) in a two-hop subnetwork
centered at each other. We also propose an emsemble technique,
Cascaded-Filtering, that combine both TSP and SS properties. Our experiments on
real Twitter datasets demonstrated that the proposed three approaches are very
practical. The proposed schemes are scalable because instead of analyzing the
whole network, they inspect user-centered two hop social networks. Our
performance study showed that proposed methods yield significantly better
performance than prior scheme in terms of true positives and false positives.
