On the discovery of social roles in large scale social systems

TL;DR

This paper introduces a data-driven method for discovering social roles in large-scale social systems using conditional triad censuses of user ego-networks, demonstrated on Facebook and Wikipedia data.

Contribution

It presents a novel approach that leverages local network structures to identify social roles, advancing the understanding of social dynamics in large online communities.

Findings

Successfully identified distinct social roles in Facebook and Wikipedia networks.

The method captures social forces influencing user interactions.

Provides a scalable way to analyze complex social systems.

Abstract

The social role of a participant in a social system is a label conceptualizing the circumstances under which she interacts within it. They may be used as a theoretical tool that explains why and how users participate in an online social system. Social role analysis also serves practical purposes, such as reducing the structure of complex systems to rela- tionships among roles rather than alters, and enabling a comparison of social systems that emerge in similar contexts. This article presents a data-driven approach for the discovery of social roles in large scale social systems. Motivated by an analysis of the present art, the method discovers roles by the conditional triad censuses of user ego-networks, which is a promising tool because they capture the degree to which basic social forces push upon a user to interact with others. Clusters of censuses, inferred from samples of large scale network carefully chosen to preserve local structural prop- erties, define the social roles. The promise of the method is demonstrated by discussing and discovering the roles that emerge in both Facebook and Wikipedia. The article con- cludes with a discussion of the challenges and future opportunities in the discovery of social roles in large social systems.