A Measure of Polarization on Social Media Networks Based on Community Boundaries

Authors

  • Pedro Guerra Federal University of Minas Gerais (UFMG)
  • Wagner Meira Jr. Federal University of Minas Gerais (UFMG)
  • Claire Cardie Cornell University
  • Robert Kleinberg Cornell University

DOI:

https://doi.org/10.1609/icwsm.v7i1.14421

Keywords:

social networks, polarization, opinion analysis

Abstract

Polarization in social media networks is a fact in several scenarios such as political debates and other contexts such as same-sex marriage, abortion and gun control. Understanding and quantifying polarization is a long-term challenge to researchers from several areas, also being a key information for tasks such as opinion analysis. In this paper, we perform a systematic comparison between social networks that arise from both polarized and non-polarized contexts. This comparison shows that the traditional polarization metric -modularity - is not a direct measure of antagonism between groups, since non-polarized networks may be also divided into fairly modular communities. To bridge this conceptual gap, we propose a novel polarization metric based on the analysis of the boundary of a pair of (potentially polarized) communities, which better captures the notions of antagonism and polarization. We then characterize polarized and non-polarized social networks according to the concentration of high-degree nodes in the boundary of communities, and found that polarized networks tend to exhibit low concentration of popular nodes along the boundary. To demonstrate the usefulness of our polarization measures, we analyze opinions expressed on Twitter on the gun control issue in the United States, and conclude that our novel metrics help making sense of opinions expressed on online media.

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Published

2021-08-03

How to Cite

Guerra, P., Meira Jr., W., Cardie, C., & Kleinberg, R. (2021). A Measure of Polarization on Social Media Networks Based on Community Boundaries. Proceedings of the International AAAI Conference on Web and Social Media, 7(1), 215-224. https://doi.org/10.1609/icwsm.v7i1.14421