[论文] Unifying Models of Intergroup Hostility in Online Discourse

论文概要

研究领域: NLP 作者: Patrick Gerard, Julia Mendelsohn, Kristina Lerman 发布时间: 2026-09-17 arXiv: 2609.20808

中文摘要

针对社会群体的敌意言论可以使排斥正常化、为不当待遇辩护，并加剧极化和政治暴力。在线言论中敌意言论的审核工作借鉴了社会心理学、道德心理学和政治科学的基础理论。然而，这些理论大多平行发展，往往提出不同甚至相互冲突的敌意发展解释，且很少在真实话语中相互检验。结果是，我们对敌意的修辞机制理解碎片化，不清楚它们在真实世界话语中如何表现、彼此关联。利用2024年美国总统大选期间来自 TikTok、Truth Social 和 Twitter/X 的286万条帖子，我们在一个共同的实证框架内对六种群体间敌意基础理论的机制进行建模——边界建构、威胁建构、替罪羊、负面评价、去人化和行动导向——以恢复群体间敌意言论的更大组织格局。在结构上，我们发现边界建构和威胁建构锚定了整个系统；在时间上，这些机制倾向于遵循一个规律的顺序：边界建构、贬损和行动导向往往出现较早；去人化和威胁建构较晚；替罪羊最晚。绘制这些理论框架在话语中的实际表现，弥合了社会科学传统之间长期存在的分歧，为计算社会科学提供了更清晰的实证基础，以超越单一标签检测来建模群体间敌意言论。

原文摘要

Hostile rhetoric toward social groups can normalize exclusion and justify mistreatment, as well as contribute to rising polarization and political violence. Efforts to moderate hostile rhetoric in online speech draw on foundational theories in social and moral psychology, and political science. However, these theories were developed largely in parallel, often propose different and sometimes conflicting accounts of how hostility develops, and have rarely been tested against each other in real discourse. The result is a fragmented understanding of the rhetorical mechanisms of hostility, without a clear sense of how they appear, and relate to each other, in real-world discourse. Using 2.86 million posts from TikTok, Truth Social, and Twitter/X during the 2024 U.S. presidential election, we mo...

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