Discovering Influencers in Opinion Formation over Social Graphs

TL;DR

This paper presents a method to analyze social learning dynamics on networks, enabling the discovery of influential agents, network topology, and information flow, even in changing environments, demonstrated on Twitter data.

Contribution

It introduces an algorithm that uncovers influence, informativeness, and network structure from belief exchange data in social learning models, adaptable to non-stationary settings.

Findings

Identified influential agents in Twitter subnetworks.

Quantified pairwise influences among agents.

Revealed underlying network topology from belief exchanges.

Abstract

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observations and exchange the beliefs with their neighbors. In this work, it is shown how the sequence of publicly exchanged beliefs over time allows users to discover rich information about the underlying network topology and about the flow of information over the graph. In particular, it is shown that it is possible (i) to identify the influence of each individual agent to the objective of truth learning, (ii) to discover how well-informed each agent is, (iii) to quantify the pairwise influences between agents, and (iv) to learn the underlying network topology. The algorithm derived herein is also able to work under non-stationary environments where either the true state of nature or the graph topology are allowed to drift over time. We apply the proposed algorithm to different subnetworks of Twitter users, and identify the most influential and central agents by using their public tweets (posts).