Organizations measure their social audience based
on the number of users, fans, and followers on social media. Every
social media platform has its user identity and a single user is
present across varied platforms. Due to the disconnected user
profiles, identifying duplicate users across media is non-trivial.
There is a need to create a complete view of a user for various
applications such as targeting and user profile construction. This
view is not easily available due to the individual identities. In this
work, we explore the feature space across social media that can
be leveraged for intelligent user identity aggregation. Further, we
present a two-phased unified identity creation process using our
feature analysis, unsupervised candidate selection, and supervised
user matching algorithms on four different social networks.