标题： 对极化的同情：对2024年7月特朗普遇刺事件社交媒体帖子的计算话语分析 显示英文标题

标题： Sympathy over Polarization: A Computational Discourse Analysis of Social Media Posts about the July 2024 Trump Assassination Attempt

摘要： 2024年7月13日，在宾夕法尼亚州的特朗普集会上，有人试图刺杀共和党总统候选人唐纳德·特朗普。 这次企图在社交媒体上引发了大规模讨论。 我们收集了刺杀尝试前后一周来自X（以前称为Twitter）的帖子，并旨在模拟这种“冲击”对公众意见和讨论主题的短期影响。 具体来说，我们的研究解决了三个关键问题：首先，我们研究公众对唐纳德·Trump的态度随时间和地区的变化（RQ1），并检查刺杀企图本身是否在不考虑现有政治立场的情况下显著影响公众态度（RQ2）。 最后，我们探讨危机前后的在线对话的主要主题，说明讨论主题如何对此类政治敏感事件作出反应（RQ3）。 通过整合基于大语言模型的情感分析、差异分析建模和主题建模技术，我们发现，在尝试之后，公众反应总体上是对特朗普表示同情，而不是两极分化，尽管存在基本的意识形态和地区差异。 显示英文摘要

摘要： 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.