用社交媒体数据实时量化群体情感对立，揭示政治极化背后的爱恨机制。
In-Group Love, Out-Group Hate: A Framework to Measure Affective Polarization via Contentious Online Discussions
- 基于离散选择模型捕捉极化网络中的决策行为
- 实证发现疫情期间口罩与封城态度快速分化
- 适合研究社会极化、数字对话治理的学者与政策制定者
情感极化指意识形态群体间因内群体喜爱与外群体憎恨而产生的感情分裂，在美国日益加剧，推动了新冠疫情中戴口罩和封城等争议议题。尽管其社会影响显著，现有意见演变模型未能纳入情感动态，也缺乏对情感极化的稳健、实时量化方法。本文提出一种离散选择模型，刻画极化社交网络中的决策过程，并设计统计推断方法，从社交媒体数据中估计关键参数——内群体喜爱与外群体憎恨。通过疫情相关在线讨论的实证验证，结果表明该方法能准确捕捉现实中的极化动态，并解释了口罩与封城态度的快速政党分化。该框架可应用于追踪各类争议议题中的情感极化，对促进数字空间中的建设性对话具有广泛意义。
原文摘要 · Abstract (English)
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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