@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://preview.aclanthology.org/fix-sig-urls/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."
}