分析特朗普遇刺事件后社交媒体情绪，发现公众反应以同情为主而非加剧分裂。
Sympathy over Polarization: A Computational Discourse Analysis of Social Media Posts about the July 2024 Trump Assassination Attempt
- 用大语言模型分析推文情绪，结合差分法和主题建模研究事件影响。
- 事件后公众对特朗普同情度上升，未因原有政治立场而明显分化。
- 适合关注社会舆情、政治传播与危机公共讨论的研究者阅读。
2024年7月13日，特朗普在宾夕法尼亚州的集会上遭遇刺杀企图，引发社交媒体大规模讨论。我们收集了事件前后一周内来自X（原推特）的帖子，旨在建模此类“冲击”对公众意见和讨论话题的短期影响。研究聚焦三个问题：首先，考察公众对特朗普的情感随时间与地区的变化（RQ1）；其次，检验刺杀事件本身是否在不依赖原有政治立场的情况下显著改变公众态度（RQ2）；最后，探讨事件前后网络对话的主要主题演变，揭示政治敏感事件中的议题变迁（RQ3）。通过融合大语言模型情感分析、差分法建模与主题建模技术，研究发现，尽管存在既有的意识形态与区域差异，事件后公众对特朗普的反应总体以同情为主，而非加剧极化。
原文摘要 · Abstract (English)
On July 13, 2024, at the Trump rally in Pennsylvania, someone attempted to assassinate Republican Presidential Candidate Donald Trump. This attempt sparked a large-scale discussion on social media. We collected posts from X (formerly known as Twitter) one week before and after the assassination attempt and aimed to model the short-term effects of such a ``shock'' on public opinions and discussion topics. Specifically, our study addresses three key questions: first, we investigate how public sentiment toward Donald Trump shifts over time and across regions (RQ1) and examine whether the assassination attempt itself significantly affects public attitudes, independent of the existing political alignments (RQ2). Finally, we explore the major themes in online conversations before and after the crisis, illustrating how discussion topics evolved in response to this politically charged event (RQ3). By integrating large language model-based sentiment analysis, difference-in-differences modeling, and topic modeling techniques, we find that following the attempt the public response was broadly sympathetic to Trump rather than polarizing, despite baseline ideological and regional disparities.
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