A systematic comparison of community detection algorithms for measuring selective exposure in co-exposure networks

Abstract The use of community detection techniques for understanding audience fragmentation and selective exposure to information has received substantial scholarly attention in recent years. However, there exists no systematic comparison, that seeks to identify which of the many community detection...

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Autor principal:	Subhayan Mukerjee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d405148f83a54bfca3770616115894d1
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spelling	oai:doaj.org-article:d405148f83a54bfca3770616115894d12021-12-02T16:24:22ZA systematic comparison of community detection algorithms for measuring selective exposure in co-exposure networks10.1038/s41598-021-94724-12045-2322https://doaj.org/article/d405148f83a54bfca3770616115894d12021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94724-1https://doaj.org/toc/2045-2322Abstract The use of community detection techniques for understanding audience fragmentation and selective exposure to information has received substantial scholarly attention in recent years. However, there exists no systematic comparison, that seeks to identify which of the many community detection algorithms are the best suited for studying these dynamics. In this paper, I address this question by proposing a formal mathematical model for audience co-exposure networks by simulating audience behavior in an artificial media environment. I show how a variety of synthetic audience overlap networks can be generated by tuning specific parameters, that control various aspects of the media environment and individual behavior. I then use a variety of community detection algorithms to characterize the level of audience fragmentation in these synthetic networks and compare their performances for different combinations of the model parameters. I demonstrate how changing the manner in which co-exposure networks are constructed significantly improves the performances of some of these algorithms. Finally, I validate these findings using a novel empirical data-set of large-scale browsing behavior. The contributions of this research are two-fold: first, it shows that two specific algorithms, FastGreedy and Multilevel are the best suited for measuring selective exposure patterns in co-exposure networks. Second, it demonstrates the use of formal modeling for informing analytical choices for better capturing complex social phenomena.Subhayan MukerjeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Subhayan Mukerjee A systematic comparison of community detection algorithms for measuring selective exposure in co-exposure networks
description	Abstract The use of community detection techniques for understanding audience fragmentation and selective exposure to information has received substantial scholarly attention in recent years. However, there exists no systematic comparison, that seeks to identify which of the many community detection algorithms are the best suited for studying these dynamics. In this paper, I address this question by proposing a formal mathematical model for audience co-exposure networks by simulating audience behavior in an artificial media environment. I show how a variety of synthetic audience overlap networks can be generated by tuning specific parameters, that control various aspects of the media environment and individual behavior. I then use a variety of community detection algorithms to characterize the level of audience fragmentation in these synthetic networks and compare their performances for different combinations of the model parameters. I demonstrate how changing the manner in which co-exposure networks are constructed significantly improves the performances of some of these algorithms. Finally, I validate these findings using a novel empirical data-set of large-scale browsing behavior. The contributions of this research are two-fold: first, it shows that two specific algorithms, FastGreedy and Multilevel are the best suited for measuring selective exposure patterns in co-exposure networks. Second, it demonstrates the use of formal modeling for informing analytical choices for better capturing complex social phenomena.
format	article
author	Subhayan Mukerjee
author_facet	Subhayan Mukerjee
author_sort	Subhayan Mukerjee
title	A systematic comparison of community detection algorithms for measuring selective exposure in co-exposure networks
title_short	A systematic comparison of community detection algorithms for measuring selective exposure in co-exposure networks
title_full	A systematic comparison of community detection algorithms for measuring selective exposure in co-exposure networks
title_fullStr	A systematic comparison of community detection algorithms for measuring selective exposure in co-exposure networks
title_full_unstemmed	A systematic comparison of community detection algorithms for measuring selective exposure in co-exposure networks
title_sort	systematic comparison of community detection algorithms for measuring selective exposure in co-exposure networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d405148f83a54bfca3770616115894d1
work_keys_str_mv	AT subhayanmukerjee asystematiccomparisonofcommunitydetectionalgorithmsformeasuringselectiveexposureincoexposurenetworks AT subhayanmukerjee systematiccomparisonofcommunitydetectionalgorithmsformeasuringselectiveexposureincoexposurenetworks
_version_	1718384140522356736

A systematic comparison of community detection algorithms for measuring selective exposure in co-exposure networks

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