Echo Chambers and Segregation in Social Networks: Markov Bridge Models and Estimation

TL;DR

This paper introduces a novel Markov bridge model to represent and estimate echo chambers and segregation in social networks, validated with real data and improved filtering algorithms.

Contribution

It presents a new community-based Markov bridge model for social segregation and develops advanced estimation algorithms for noisy social network data.

Findings

The Markov bridge model effectively captures community segregation dynamics.

The Bayesian filtering algorithm outperforms traditional hidden Markov models.

Numerical results demonstrate accurate estimation of segregation levels.

Abstract

This paper deals with the modeling and estimation of the sociological phenomena called echo chambers and segregation in social networks. Specifically, we present a novel community-based graph model that represents the emergence of segregated echo chambers as a Markov bridge process. A Markov bridge is a one-dimensional Markov random field that facilitates modeling the formation and disassociation of communities at deterministic times which is important in social networks with known timed events. We justify the proposed model with six real world examples and examine its performance on a recent Twitter dataset. We provide model parameter estimation algorithm based on maximum likelihood and, a Bayesian filtering algorithm for recursively estimating the level of segregation using noisy samples obtained from the network. Numerical results indicate that the proposed filtering algorithm outperforms the conventional hidden Markov modeling in terms of the mean-squared error. The proposed filtering method is useful in computational social science where data-driven estimation of the level of segregation from noisy data is required.