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

Rui Luo; Buddhika Nettasinghe; Vikram Krishnamurthy

doi:10.1109/TCSS.2021.3091168

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

Journal article

Peer reviewed

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

Rui Luo, Buddhika Nettasinghe and Vikram Krishnamurthy

IEEE transactions on computational social systems, Vol.9(3), pp.891-901

06/2022

DOI: 10.1109/TCSS.2021.3091168

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Abstract

This article 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 (MB) process. An MB is a 1-D 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 real-world examples and examine its performance on a recent Twitter dataset. We provide a 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.

Bayesian filtering

Blogs

Companies

Computational modeling

echo chamber

Hidden Markov models

Markov bridge (MB)

Numerical models

segregation

social network

Social networking (online)

Voting

Details

Title: Subtitle: Echo Chambers and Segregation in Social Networks: Markov Bridge Models and Estimation
Creators: Rui Luo - Cornell University
Buddhika Nettasinghe - Cornell University
Vikram Krishnamurthy - Cornell University
Resource Type: Journal article
Publication Details: IEEE transactions on computational social systems, Vol.9(3), pp.891-901
Publisher: IEEE
DOI: 10.1109/TCSS.2021.3091168
ISSN: 2329-924X
eISSN: 2329-924X
Grant note: W911NF-19-1-0365 / U.S. Army Research Office (10.13039/100000183) CCF-2112457; CCF-1714180 / National Science Foundation (10.13039/100000001)
Language: English
Date published: 06/2022
Academic Unit: Business Analytics
Record Identifier: 9984422857702771

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