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

TL;DR

This study analyzes social media reactions to the July 2024 Trump assassination attempt, revealing that public sentiment was broadly sympathetic rather than polarized, using computational methods on large-scale online discourse.

Contribution

It introduces a combined approach of sentiment analysis, difference-in-differences, and topic modeling to study short-term public opinion shifts after a political crisis.

Findings

Public sentiment shifted towards sympathy for Trump

Discussion topics evolved in response to the event

Baseline ideological differences did not lead to polarization

Abstract

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.