Online Polarization and Violence in the United States: A Computational Analysis of Viewer Comments on Partisan News Coverage of President Donald Trump’s First Assassination Attempt

This study analyzes 15,000 user comments from ideologically distinct news platforms—Fox News (right-leaning), ABC News (left-leaning), and BBC News (centrist)—to investigate how political ideology shapes emotional expression and engagement following the July 2024 assassination attempt on President Donald Trump in Pennsylvania. We examine whether right-leaning comments exhibit elevated anger and hate speech, whether left-leaning comments show greater empathy and positive affect, and how emotional intensity influences engagement metrics such as likes and shares. Using computational science methods, we trace the evolution of emotional tone and toxicity over time, focusing on affective polarization triggered by a high-salience violent event. Our findings reveal that ideological alignment amplifies engagement with congruent emotional content, reinforcing echo chambers and intensifying online hostility. We address gaps in understanding how ideological echo chambers amplify polarized emotions and online hostility following a high-profile violent event, emphasizing the place of emotions in contemporary political communication and digital news consumption.

Authors

Institutions

Publication Details

Journal
Social Science Computer Review
Published
2026-02-17
DOI
https://doi.org/10.1177/08944393261426534
Primary Topic
Computational and Text Analysis Methods
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Online Polarization and Violence in the United States: A Computational Analysis of Viewer Comments on Partisan News Coverage of President Donald Trump’s First Assassination Attempt

AC Okeke, Dennis Ekwemnachukwu Okeke, Christiana Ibiwoye, Ousman Mbaye et al.
Social Science Computer Review
Computational and Text Analysis Methods
article

Online Polarization and Violence in the United States: A Computational Analysis of Viewer Comments on Partisan News Coverage of President Donald Trump’s First Assassination Attempt

AC Okeke, Dennis Ekwemnachukwu Okeke, Christiana Ibiwoye, Ousman Mbaye, Chibuzor Peter Okpala, Margaret Adutwumwaa Boateng, Isaac Ebuka Obah
article en

Abstract

This study analyzes 15,000 user comments from ideologically distinct news platforms—Fox News (right-leaning), ABC News (left-leaning), and BBC News (centrist)—to investigate how political ideology shapes emotional expression and engagement following the July 2024 assassination attempt on President Donald Trump in Pennsylvania. We examine whether right-leaning comments exhibit elevated anger and hate speech, whether left-leaning comments show greater empathy and positive affect, and how emotional intensity influences engagement metrics such as likes and shares. Using computational science methods, we trace the evolution of emotional tone and toxicity over time, focusing on affective polarization triggered by a high-salience violent event. Our findings reveal that ideological alignment amplifies engagement with congruent emotional content, reinforcing echo chambers and intensifying online hostility. We address gaps in understanding how ideological echo chambers amplify polarized emotions and online hostility following a high-profile violent event, emphasizing the place of emotions in contemporary political communication and digital news consumption.

Social Science Computer Review
Manchester Metropolitan University (GB), University of Colorado Boulder (US), Nnamdi Azikiwe University (NG), University of Colorado System (US), University of Wisconsin–Milwaukee (US), National Open University of Nigeria (NG), University of Louisiana at Lafayette (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 67%
Computational and Text Analysis Methods
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Online Polarization and Violence in the United States: A Computational Analysis of Viewer Comments on Partisan News Coverage of President Donald Trump’s First Assassination Attempt — AC Okeke, Dennis Ekwemnachukwu Okeke, et al. · Social Science Computer Review (2026) | TGRS Research Map | TGRS