A cognitive categorization-based approach for understanding identity representation online

Abstract

Computationally representing social identities using social networking profiles traditionally involves the reduction of identities to fit into simplistic categories such as "friends." In contrast, this thesis proposes that the data structures underlying user identities can be algorithmically processed and interpreted in ways that assist in understanding more nuanced aspects of identity such as "subculture" or"personality" Building upon an interdisciplinary computational identity model developed by Fox Harrell in his NSF-supported Advanced Identity Representation Project, this thesis proposes an algorithm based on theories of cognitive categorization[6, 7] to reveal implicit categories in computational identity systems. The algorithm has been applied to social networking site Facebook and a suite of graphical user interfaces was developed to enable users to explore individual and group identities. In a qualitative study, we found that most of the generated categories coherently represented social groups and would be useful for applications such as expressing the groups' collective identities.