Abstract

Political podcasts and video channels have become increasingly influential sources of news, commentary, and ideological framing in the digital media ecosystem, yet limited Information Systems research has systematically examined how ideological narratives differ across left-wing and right-wing podcasters and how these differences relate to digital engagement. Prior research has shown that social media platforms increasingly shape political discourse and polarization (Cinelli et al., 2021), while YouTube has emerged as an important platform for political communication and audience interaction (Lewis, 2020). This study investigates the relationships among ideological narratives, political discourse, and audience engagement using large-scale YouTube podcast data from politically aligned content creators in the United States. We compile a dataset consisting of more than 10,000 YouTube video comments, together with video metadata and transcripts collected from leading left-wing and right-wing podcasters identified through public rankings and social media sources. Using natural language processing techniques such as topic modeling and sentiment analysis (Hutto & Gilbert, 2014; Blei et al., 2003), our findings reveal substantial differences in narrative structures, sentiment distributions, and audience interaction patterns across ideological groups. In particular, right-wing podcasters employ more emotionally charged and politically focused narratives, whereas left-wing channels exhibit comparatively broader thematic diversity. Audience comments also demonstrate distinguishable engagement and sentiment dynamics between ideological groups. This study contributes to the Information Systems literature by examining podcasts as an increasingly important form of digital political communication and demonstrates the value of computational social science methods for understanding ideological narratives and online engagement in contemporary social media ecosystems.

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