Community detection and role identification in directed networks: understanding the Twitter network of the care.data debate

TL;DR

This paper applies graph-theoretic methods to analyze the directed Twitter network of the care.data debate, uncovering communities and user roles to enhance understanding of information flow for policy communication.

Contribution

It introduces methods that account for directionality in social networks, revealing community structures and user roles in Twitter's information propagation.

Findings

Identified robust communities reflecting information flow

Classified users by roles in directing information

Methods applicable to directed social networks

Abstract

With the rise of social media as an important channel for the debate and discussion of public affairs, online social networks such as Twitter have become important platforms for public information and engagement by policy makers. To communicate effectively through Twitter, policy makers need to understand how influence and interest propagate within its network of users. In this chapter we use graph-theoretic methods to analyse the Twitter debate surrounding NHS England's controversial care.data scheme. Directionality is a crucial feature of the Twitter social graph - information flows from the followed to the followers - but is often ignored in social network analyses; our methods are based on the behaviour of dynamic processes on the network and can be applied naturally to directed networks. We uncover robust communities of users and show that these communities reflect how information flows through the Twitter network. We are also able to classify users by their differing roles in directing the flow of information through the network. Our methods and results will be useful to policy makers who would like to use Twitter effectively as a communication medium.