A Heterophily-Based Polarization Measure for Multi-community Networks

Sreeja Nair, Adriana Iamnitchi

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

Abstract

This work proposes a heterophily-based metric for quantifying polarization in social networks where multiple ideological, antagonistic communities coexist. This metric captures node-level polarization and is built on user’s affinity towards other communities rather than their own. Node-level values can then be aggregated at the community, network, or sub-network level, providing a more detailed map of polarization. We tested our metric on the Polblogs network, White Helmets Twitter interaction network with two communities and the VoterFraud2020 domain network with five communities. We also tested our metric on dK-random graphs to verify that it results in low polarization scores, as expected. Finally, we compared our metric with two widely used polarization measures: Guerra’s polarization index and RWC.

Original languageEnglish
Title of host publicationProceedings of the Social Informatics - 13th International Conference 2022
PublisherSpringer
Pages459-471
Number of pages13
ISBN (Electronic)978-3-031-19096-4
DOIs
Publication statusPublished - 2022
Event13th International Conference on Social Informatics - Glasgow, United Kingdom
Duration: 19 Oct 202221 Oct 2022
Conference number: 13
https://www.dcs.gla.ac.uk/socinfo2022/

Publication series

SeriesLecture Notes in Computer Science
Volume13618
ISSN0302-9743

Conference

Conference13th International Conference on Social Informatics
Abbreviated titleSocInfo 2022
Country/TerritoryUnited Kingdom
CityGlasgow
Period19/10/2221/10/22
Internet address

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