Deriving interpersonal role identities from social network interactions

In recent years, social networking sites (SNS) have been transformed into virtual societies where users express their feelings, share opinions, and socialize with friends, families, and co-workers. Users in these sites are still connected with each other with so called friends/followers relationships which are not representative of the real life role identities. However, a single person can play multiple roles in a society. For example, a person has a family member role identity with his wife, professional member role identity with his colleagues and academic member role identity with his class fellows. Thus, the same person can interact differently with different people based on her role identity with her counterpart. In this paper, we have predicted interpersonal role identities (e.g., family members, academic members, professional members, friends, and acquaintances) of a user with other connected members in an egocentric network (e.g., Facebook) from their word use patterns during interactions. We have proposed a weighted hybrid machine learning based model to predict the role identities from users' word usage patterns. We have also validated our experiment results by using the datasets of both Facebook and Twitter.