  Online Social Networks (OSNs) play an important role for internet users to
carry out their daily activities like content sharing, news reading, posting
messages, product reviews and discussing events etc. At the same time, various
kinds of spammers are also equally attracted towards these OSNs. These cyber
criminals including sexual predators, online fraudsters, advertising
campaigners, catfishes, and social bots etc. exploit the network of trust by
various means especially by creating fake profiles to spread their content and
carry out scams. All these malicious identities are very harmful for both the
users as well as the service providers. From the OSN service provider point of
view, fake profiles affect the overall reputation of the network in addition to
the loss of bandwidth. To spot out these malicious users, huge manpower effort
and more sophisticated automated methods are needed. In this paper, various
types of OSN threat generators like compromised profiles, cloned profiles and
online bots (spam bots, social bots, like bots and influential bots) have been
classified. An attempt is made to present several categories of features that
have been used to train classifiers in order to identify a fake profile.
Different data crawling approaches along with some existing data sources for
fake profile detection have been identified. A refresher on existing cyber laws
to curb social media based cyber crimes with their limitations is also
presented.
