In-Group Love, Out-Group Hate: A Framework to Measure Affective Polarization via Contentious Online Discussions
This work addresses the societal problem of affective polarization for researchers and policymakers by providing a real-time quantification method, though it is incremental as it builds on existing models by incorporating emotional dynamics.
The authors tackled the problem of measuring affective polarization in online discussions by introducing a discrete choice model and statistical inference method to estimate in-group love and out-group hate parameters from social media data, demonstrating accurate capture of polarization dynamics and explaining the rapid partisan gap in attitudes towards COVID-19 masking and lockdowns.
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.