@inproceedings{zeng-etal-2025-sympathy,
title = "Sympathy over Polarization: A Computational Discourse Analysis of Social Media Posts about the {J}uly 2024 Trump Assassination Attempt",
author = "Zeng, Qingcheng and
Liu, Guanhong and
Xue, Zhaoqian and
Ford, Diego and
Voigt, Rob and
Hagen, Loni and
Li, Lingyao",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-ijcnlp.4/",
pages = "56--68",
ISBN = "979-8-89176-303-6",
abstract = "On July 13, 2024, an assassination attempt was made on Republican presidential candidate Donald Trump during a rally in Pennsylvania. This event triggered widespread discourses on social media platforms. In this study, we analyze posts from X (formerly Twitter) collected during the week preceding and following the incident to examine the short-term impact of this political shock on public opinion and discourse. Our investigation is guided by three central research questions. First (RQ1), we assess how public stance toward Donald Trump evolved over time and varied across geographic regions. Second (RQ2), we apply causal inference methods to determine whether the assassination attempt itself significantly influenced public attitudes, independent of pre-existing political alignments. Third (RQ3), we conduct topic modeling to identify shifts in dominant themes of online discussions before and after the event. Integrating large language model-based stance detection, difference-in-differences estimation, and topic modeling, our findings reveal a marked surge in sympathetic responses toward Trump in the immediate aftermath of the attempt, suggesting a unifying effect that temporarily transcended ideological and regional divides."
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%0 Conference Proceedings
%T Sympathy over Polarization: A Computational Discourse Analysis of Social Media Posts about the July 2024 Trump Assassination Attempt
%A Zeng, Qingcheng
%A Liu, Guanhong
%A Xue, Zhaoqian
%A Ford, Diego
%A Voigt, Rob
%A Hagen, Loni
%A Li, Lingyao
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-303-6
%F zeng-etal-2025-sympathy
%X On July 13, 2024, an assassination attempt was made on Republican presidential candidate Donald Trump during a rally in Pennsylvania. This event triggered widespread discourses on social media platforms. In this study, we analyze posts from X (formerly Twitter) collected during the week preceding and following the incident to examine the short-term impact of this political shock on public opinion and discourse. Our investigation is guided by three central research questions. First (RQ1), we assess how public stance toward Donald Trump evolved over time and varied across geographic regions. Second (RQ2), we apply causal inference methods to determine whether the assassination attempt itself significantly influenced public attitudes, independent of pre-existing political alignments. Third (RQ3), we conduct topic modeling to identify shifts in dominant themes of online discussions before and after the event. Integrating large language model-based stance detection, difference-in-differences estimation, and topic modeling, our findings reveal a marked surge in sympathetic responses toward Trump in the immediate aftermath of the attempt, suggesting a unifying effect that temporarily transcended ideological and regional divides.
%U https://aclanthology.org/2025.findings-ijcnlp.4/
%P 56-68
Markdown (Informal)
[Sympathy over Polarization: A Computational Discourse Analysis of Social Media Posts about the July 2024 Trump Assassination Attempt](https://aclanthology.org/2025.findings-ijcnlp.4/) (Zeng et al., Findings 2025)
ACL