Diversity of a User's Friend Circle in OSNs and Its Use for Profiling.

In past studies, Online Social Networks (OSNs) is commonly assumed to build on triadic closure, implying that alters in a user's ego network form either a single connected component, or a small number of connected components. In real-world OSNs, we find a significant number of users with a different ego network pattern consisting of a more diverse social circle with many friends not connected to each other. We conjecture this is caused by the increasing use of OSNs for functional (e.g. business or marketing) rather than traditional socializing activities. We refer to the resulting prototypical users as functional and social users respectively. In this paper, we use a manually tagged dataset (from Tencent social platform) to identify these two type of users and demonstrate their different friend circle patterns using examples. To help sort out functional users from social users, we develop metrics to measure diversity of a user's friend circle, borrowing concepts from classic works on structural holes and community detection. We show how the different measures of diversity perform in classifying the two types of users. Then we combine the structural diversity measures and behavioral measures to train machine leaning models. We further study ego network diversity in groups of users with different demographics (profession, gender and age). Our results bring new insights to the heterogeneous nature of today's OSNs and help better profile users. Our study also shed new light on structural hole theory and Dunbar's number in the OSN context.