Dissertations, Theses, and Capstone Projects

Date of Degree

9-2026

Document Type

Doctoral Dissertation

Degree Name

Doctor of Philosophy

Program

Criminal Justice

Advisor

Brian Lawton

Committee Members

Brian Lawton

Kevin Wolff

Heath Brown

Dana Weinberg

Subject Categories

American Politics | Other Public Affairs, Public Policy and Public Administration | Other Sociology | Social Psychology

Keywords

authoritarian rhetoric, intergroup sentiment, political speech, podcasts, 2024 U.S. Election

Abstract

Political rhetoric surrounding key events has seen sharp increases in emotionally affective speech, intergroup sentiment, and rhetoric unbecoming of democratic societies in recent years. Similarly, semi-democratic and ideologically driven speech has become increasingly normalized within an era of political polarization and hardline beliefs in modern media. Within this economy of outrage, sensational and largely unmoderated media formats, particularly podcasts, have become a mainstay of political information and discourse. This raises an important question: how does rhetoric surrounding political events construct intergroup sentiment and authoritarian rhetoric within political media, particularly American political podcasts? Further, given political podcasts’ growing social influence in American society, how might these rhetorical strategies contribute to calls to action during events framed as moral or existential struggles? This study investigated how intergroup sentiment and authoritarian rhetoric manifest within American political podcasts, particularly during the politically volatile period surrounding the 2024 U.S. Presidential Election. Drawing on Intergroup Threat Theory and established research into authoritarianism and podcasts, this study explores how political podcasts, operating largely outside of media regulation, may have used emotionally charged rhetoric to reinforce collective identity and legitimize perceived threats from political opposition. Focusing on fifteen well known political podcasts from across the political spectrum over a 10-week timeline centered on November 6th, 2024, this study employed a hybrid computational-qualitative design, integrating text mining strategies, Natural Language Processing (NLP), large language models, narrative extraction, and qualitative coding. The findings within this study contribute to empirical study on political communication, authoritarianism, and media studies by demonstrating how podcast discourse may serve to normalize political polarization in democratic societies.

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