@inproceedings{balci-etal-2025-podcast,
title = "Podcast Outcasts: Understanding Rumble{'}s Podcast Dynamics",
author = "Balci, Utkucan and
Patel, Jay and
Balci, Berkan and
Blackburn, Jeremy",
editor = {H{\"a}m{\"a}l{\"a}inen, Mika and
{\"O}hman, Emily and
Bizzoni, Yuri and
Miyagawa, So and
Alnajjar, Khalid},
booktitle = "Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities",
month = may,
year = "2025",
address = "Albuquerque, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.nlp4dh-1.6/",
doi = "10.18653/v1/2025.nlp4dh-1.6",
pages = "48--62",
ISBN = "979-8-89176-234-3",
abstract = "The rising popularity of podcasts as an emerging medium opens new avenues for digital humanities research, particularly when examining video-based media on alternative platforms. We present a novel data analysis pipeline for analyzing over 13K podcast videos (526 days of video content) from Rumble and YouTube that integrates advanced speech-to-text transcription, transformer-based topic modeling, and contrastive visual learning. We uncover the interplay between spoken rhetoric and visual elements in shaping political bias. Our findings reveal a distinct right-wing orientation in Rumble{'}s podcasts, contrasting with YouTube{'}s more diverse and apolitical content. By merging computational techniques with comparative analysis, our study advances digital humanities by demonstrating how large-scale multimodal analysis can decode ideological narratives in emerging media format."
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%0 Conference Proceedings
%T Podcast Outcasts: Understanding Rumble’s Podcast Dynamics
%A Balci, Utkucan
%A Patel, Jay
%A Balci, Berkan
%A Blackburn, Jeremy
%Y Hämäläinen, Mika
%Y Öhman, Emily
%Y Bizzoni, Yuri
%Y Miyagawa, So
%Y Alnajjar, Khalid
%S Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, USA
%@ 979-8-89176-234-3
%F balci-etal-2025-podcast
%X The rising popularity of podcasts as an emerging medium opens new avenues for digital humanities research, particularly when examining video-based media on alternative platforms. We present a novel data analysis pipeline for analyzing over 13K podcast videos (526 days of video content) from Rumble and YouTube that integrates advanced speech-to-text transcription, transformer-based topic modeling, and contrastive visual learning. We uncover the interplay between spoken rhetoric and visual elements in shaping political bias. Our findings reveal a distinct right-wing orientation in Rumble’s podcasts, contrasting with YouTube’s more diverse and apolitical content. By merging computational techniques with comparative analysis, our study advances digital humanities by demonstrating how large-scale multimodal analysis can decode ideological narratives in emerging media format.
%R 10.18653/v1/2025.nlp4dh-1.6
%U https://aclanthology.org/2025.nlp4dh-1.6/
%U https://doi.org/10.18653/v1/2025.nlp4dh-1.6
%P 48-62
Markdown (Informal)
[Podcast Outcasts: Understanding Rumble’s Podcast Dynamics](https://aclanthology.org/2025.nlp4dh-1.6/) (Balci et al., NLP4DH 2025)
ACL
- Utkucan Balci, Jay Patel, Berkan Balci, and Jeremy Blackburn. 2025. Podcast Outcasts: Understanding Rumble’s Podcast Dynamics. In Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities, pages 48–62, Albuquerque, USA. Association for Computational Linguistics.