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Estimating affective polarization on a social network.

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Summary
This summary is machine-generated.

Researchers developed a new network-based measure for affective polarization, assessing hostility and social distance online. This method revealed increasing polarization in COVID-19 discussions on Twitter over time.

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Area of Science:

  • Social Sciences
  • Computational Social Science
  • Communication Studies

Background:

  • Widespread concerns exist regarding polarization and hate speech on social media platforms.
  • Affective polarization, characterized by partisan hostility, is a key factor linking political disagreements to online hostile language.
  • Existing methods for measuring online affective polarization are limited and do not jointly consider hostility and social distance.

Purpose of the Study:

  • To address the methodological gap in measuring affective polarization online.
  • To propose a novel network-based measure that integrates both hostility and social distance.
  • To enable independent study of the affective and social distance components of polarization.

Main Methods:

  • Development of a network-based measure for affective polarization.
  • Jointly analyzing hostility (affective component) and social distance (interaction patterns).
  • Application of the measure to a large-scale Twitter dataset concerning COVID-19 discussions.

Main Results:

  • The proposed measure accurately reflects the relationship between disagreement levels and expressed hostility.
  • It also captures the social distance component, indicating choices in interaction and avoidance.
  • Analysis of COVID-19 Twitter data showed low affective polarization in February 2020, increasing significantly in subsequent months.

Conclusions:

  • The new network-based measure effectively quantifies online affective polarization by combining hostility and social distance.
  • Affective polarization on Twitter regarding COVID-19 discussions escalated as user participation grew.
  • This methodology offers a more comprehensive approach to understanding online partisan animosity.