Identifying Users across Multiple Online Social Networks

An online user joins multiple social networks in order to enjoy different services. On each joined social network, she creates an identity and constitutes its three major dimensions namely profile, content and connection network. She largely governs her identity formulation on any social network and therefore can manipulate multiple aspects of it. With no global identifier to mark her presence uniquely in the online domain, her online identities remain unlinked, isolated and difficult to search. Literature has proposed identity search methods on the basis of profile attributes, but has left the other identity dimensions e.g. content and network, unexplored. In this work, we introduce two novel identity search algorithms based on content and network attributes and improve on traditional identity search algorithm based on profile attributes of a user. We apply proposed identity search algorithms to find a user’s identity on Facebook, given her identity on Twitter. We report that a combination of proposed identity search algorithms found Facebook identity for 39% of Twitter users searched while traditional method based on profile attributes found Facebook identity for only 27.4%. Each proposed identity search algorithm access publicly accessible attributes of a user on any social network. We deploy an identity resolution system, Finding Nemo, which uses proposed identity search methods to find a Twitter user’s identity on Facebook. We conclude that inclusion of more than one identity search algorithm, each exploiting distinct dimensional attributes of an identity, helps in improving the accuracy of an identity resolution process.