In-Group Love, Out-Group Hate: A Framework to Measure Affective Polarization via Contentious Online Discussions

TL;DR

This paper introduces a novel discrete choice model and inference method to quantify and track affective polarization in online discussions, especially during contentious issues like COVID-19, capturing emotional dynamics and societal divides.

Contribution

It presents a new framework for measuring affective polarization using social media data, incorporating emotional decision-making into models of opinion change.

Findings

Accurately captures real-world polarization dynamics

Explains rapid partisan attitude shifts during COVID-19

Provides a method for real-time affective polarization tracking

Abstract

Affective polarization, the emotional divide between ideological groups marked by in-group love and out-group hate, has intensified in the United States, driving contentious issues like masking and lockdowns during the COVID-19 pandemic. Despite its societal impact, existing models of opinion change fail to account for emotional dynamics nor offer methods to quantify affective polarization robustly and in real-time. In this paper, we introduce a discrete choice model that captures decision-making within affectively polarized social networks and propose a statistical inference method estimate key parameters -- in-group love and out-group hate -- from social media data. Through empirical validation from online discussions about the COVID-19 pandemic, we demonstrate that our approach accurately captures real-world polarization dynamics and explains the rapid emergence of a partisan gap in attitudes towards masking and lockdowns. This framework allows for tracking affective polarization across contentious issues has broad implications for fostering constructive online dialogues in digital spaces.