Finding Nemo: Searching and Resolving Identities of Users Across Online Social Networks

TL;DR

This paper introduces Finding Nemo, an integrated system that uses profile, content, and connection data to search and link user identities across multiple social networks, improving accuracy over previous methods.

Contribution

It presents the first integrated approach leveraging all three identity dimensions to enhance cross-network user identity search accuracy.

Findings

Integrated system outperforms single-dimension algorithms

Uses known identity on one network to find others

Validated on Twitter and Facebook datasets

Abstract

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. Earlier research has explored the above mentioned dimensions, to search and link her multiple identities with an assumption that the considered dimensions have been least disturbed across her identities. However, majority of the approaches are restricted to exploitation of one or two dimensions. We make a first attempt to deploy an integrated system (Finding Nemo) which uses all the three dimensions of an identity to search for a user on multiple social networks. The system exploits a known identity on one social network to search for her identities on other social networks. We test our system on two most popular and distinct social networks - Twitter and Facebook. We show that the integrated system gives better accuracy than the individual algorithms. We report experimental findings in the report.