EnTwine: Feature Analysis and Candidate Selection for Social User Identity Aggregation

ABSTRACT

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.

References

1. Paridhi Jain, Ponnurangam Kumaraguru, and Anupam Joshi. @ I seek 'fb. me': Identifying Users across Multiple Online Social Networks. In WWW'13 Companion.
2. Andrew McCallum, Kamal Nigam, and Lyle H Ungar. Efficient clustering of high-dimensional data sets with application to reference matching. In SIGKDD '00.

• Human-centered computing

  • Human computer interaction (HCI)

    • Interaction paradigms

• Information systems

  • Information retrieval