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

Buddhika Nettasinghe; Ashwin Rao; Bohan Jiang; Allon G. Percus; Kristina Lerman

doi:10.1145/3696410.3714935

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

Conference proceeding

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

Buddhika Nettasinghe, Ashwin Rao, Bohan Jiang, Allon G. Percus and Kristina Lerman

Proceedings of the ACM on Web Conference 2025, pp.560-575

ACM Conferences

WWW '25: The ACM Web Conference 2025

04/22/2025

DOI: 10.1145/3696410.3714935

Appears in UI Libraries Support Open Access

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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.

Applied computing -- Law, social and behavioral sciences -- Sociology

Computing methodologies -- Modeling and simulation -- Model development and analysis -- Modeling methodologies

Information systems -- World Wide Web -- Web applications -- Social networks

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Details

Title: Subtitle: In-Group Love, Out-Group Hate: A Framework to Measure Affective Polarization via Contentious Online Discussions
Creators: Buddhika Nettasinghe - Tippie College of Business, University of Iowa, Iowa City, Iowa, USA
Ashwin Rao - Department of Computer Science, USC Information Sciences Institute, Marina del Rey, California, USA
Bohan Jiang - Arizona State University
Allon G. Percus - Claremont Graduate University
Kristina Lerman - Department of Computer Science, USC Information Sciences Institute, Marina del Rey, California, USA
Resource Type: Conference proceeding
Publication Details: Proceedings of the ACM on Web Conference 2025, pp.560-575
Conference: WWW '25: The ACM Web Conference 2025
Series: ACM Conferences
DOI: 10.1145/3696410.3714935
Publisher: Association for Computing Machinery
Number of pages: 16
Grant note: National Science Foundation: CCF 2200256
This work was funded in part by the National Science Foundation under grant CCF 2200256.
Language: English
Date published: 04/22/2025
Academic Unit: Business Analytics
Record Identifier: 9984813161402771

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