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

• URL: http://arxiv.org/abs/2412.14414v1
• Date: Wed, 18 Dec 2024 23:58:13 GMT
• Title: In-Group Love, Out-Group Hate: A Framework to Measure Affective Polarization via Contentious Online Discussions
• Authors: Buddhika Nettasinghe, Ashwin Rao, Bohan Jiang, Allon Percus, Kristina Lerman,
• Abstract summary: We introduce a discrete choice model that captures decision-making within affectively polarized social networks. We propose a statistical inference method estimate key parameters -- in-group love and out-group hate -- from social media data.
• Score: 2.8963943201523796
• License:
• 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.

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