用AI分析社交媒体中移民议题的去人性化隐喻，揭示政治立场与传播效果的关系。
When People are Floods: Analyzing Dehumanizing Metaphors in Immigration Discourse with Large Language Models
- 结合词级与文档级信号，量化七类隐喻概念的使用程度
- 保守派更常使用去人性化隐喻，但不同隐喻类型差异显著
- 动物类隐喻更易被转发，尤其对自由派作者更具传播力
隐喻在政治话语中广泛存在，能深刻影响人们对重要议题的理解。本文提出一种计算方法，用于测量社交媒体中关于移民议题的隐喻语言。基于社会科学研究，识别出七类常见隐喻概念（如“水”或“害虫”）。提出并评估了一种结合词级与文档级信号的新技术，以衡量这些概念的隐喻使用强度。研究分析了40万条美国推文中的隐喻、政治立场与用户互动之间的关系。结果显示，保守派比自由派更常使用去人性化隐喻，但该效应在不同概念间差异明显。此外，涉及生物类隐喻的内容更易被转发，尤其对自由派作者而言。本研究展示了计算方法在理解政治话语中微妙且隐性语言方面的潜力。
原文摘要 · Abstract (English)
Metaphor, discussing one concept in terms of another, is abundant in politics and can shape how people understand important issues. We develop a computational approach to measure metaphorical language, focusing on immigration discourse on social media. Grounded in qualitative social science research, we identify seven concepts evoked in immigration discourse (e.g. "water" or "vermin"). We propose and evaluate a novel technique that leverages both word-level and document-level signals to measure metaphor with respect to these concepts. We then study the relationship between metaphor, political ideology, and user engagement in 400K US tweets about immigration. While conservatives tend to use dehumanizing metaphors more than liberals, this effect varies widely across concepts. Moreover, creature-related metaphor is associated with more retweets, especially for liberal authors. Our work highlights the potential for computational methods to complement qualitative approaches in understanding subtle and implicit language in political discourse.
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