Podcast Outcasts: Understanding Rumble’s Podcast Dynamics (2025)

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.

## introduction in today's world, visual elements play an important role in communication and engagement @xcite . the rise of social media, video-sharing platforms, and visual-centric content has transformed how we perceive information. this shift has transformed various media formats, including podcasts. the integration of visual elements into podcasts has given rise to video podcasts, making it increasingly popular @xcite . from joe rogan's $200m exclusivity deal with spotify @xcite , to andrew tate's misogynistic rhetoric that led him to get banned from mainstream platforms @xcite , and donald trump's podcast appearances during the 2024 us election period @xcite @xcite , the cultural impact of this medium is increasingly visible in mainstream and alternative platforms alike. for scholars in the digital humanities, these developments open new avenues for exploring how language, imagery, and ideology intersect to shape collective understandings of politics and culture. in particular, rumble, a self-described neutral video-sharing platform @xcite , hosts a range of high-profile, often deplatformed, controversial figures, e.g., donald trump, alex jones, andrew tate, who have cultivated large followings despite bans or restrictions on sites like youtube @xcite @xcite . notably, in august 2024, following the arrest of telegram's ceo in france under allegations of failing to adequately moderate content on the platform, rumble's ceo announced his departure from europe, due to concern of encountering comparable challenges to his platform @xcite . to date, no research has explored whether right-wing podcasters are merely a segment of rumble's podcasting selection or if the platform serves as a bastion for right-wing propaganda. to address this gap, our study integrates computational text analysis and visual embedding techniques with a novel data analysis pipeline to answer the following research question: how do political biases manifest in the narratives and imagery of video podcasts on rumble? drawing on 13k podcast videos, equivalent to 526 days (more than 750k minutes) of video content, from both youtube and rumble, our approach integrates speech-to-text transcription with transformer-based topic modeling and contrastive learning for image analysis. our research reveals a clear pattern: rumble exhibits a noticeable rightwing bias in its audio and visual content, whereas youtube primarily remains apolitical, concentrating on mainstream subjects. contributions. we make several contributions. first, we conduct the first large-scale data-driven study on video podcasts, where we provide a layered analysis of platform bias on video podcast speech-t o-t ext (faster whisper) t1 repr esent at ive i m ages (ph ash + h amming distance) ii i2 i1 ei e2 clust er ing (u m ap + h dbscan ) e1 collect youtube dat a sm s2 topic m odeling (bertopic) s1 topic-1 topic-2 topic-3 topic-4 visual clust er s split t ing int o sent ences gener at ing clip em beddings rem oving conser vat ional filler s + back channels clust er l abeling via codebook topics figure 1: our podcast video processing and analysis pipeline: 1) collect data from rumble and youtube, 2) extract video metadata 3) use 'faster-whisper' to generate transcriptions, 4) use bertopic with mpnet vectors to find topics, 5) sample representative images of podcast videos, 6) generate clip embeddings of the representative images to cluster them using umap and hdbscan. our methodology provides a blueprint for future studies on political multimodal content engagement in the context of evolving social platforms, particularly in video podcast content. overall, our contributions enrich digital humanities by providing new avenues for interpreting multimodal political communication and understanding the cultural dynamics of digital media. ## background & related work the term "podcast" was mentioned for the first time in 2004 @xcite . in 2006, the pew research center defined podcasting as a method of distributing audio and video content online, distinguishing it from earlier formats by enabling automatic transfers to users' devices for on-demand consumption, often on portable digital music players such as mp3 players or ipods @xcite . although the core of this definition remains relevant, the influence of podcasts has evolved remarkably over time. particularly with the widespread use of social media, podcasts are now viewed by millions of users on video streaming platforms @xcite . over a 12-month period spanning parts of both 2022 and 2023, nearly half of the us adult population reported having listened to a podcast, with one-fifth frequently doing so multiple times a week @xcite . this proportion increases to one-third among young adults under 30. of the u.s. adults who listened to a podcast during this interval, 46% were republicans and 54% democrats, with 65% of republican and 69% of democratic listeners tuning into news-related podcasts. podcasts as vectors of political discourse. a range of studies have explored the impact of political podcasts on individuals' political engagement and attitudes @xcite @xcite @xcite @xcite . notably, consuming podcasts is linked to heightened levels of personalized politics, a process where individuals integrate new information into their existing ideological frameworks to develop more personalized political understandings @xcite . @xcite further explored the relationship between partisan podcast consumption, emotional responses, and political participation, finding that selective exposure to partisan podcasts can shape emotional reactions to political candidates, thereby affecting political engagement. @xcite also observed a positive correlation between using podcasts for news and increased political participation, suggesting that podcasts might boost political involvement among individuals. rizwan et al. @xcite analyzed over 9,000 episodes from 31 u.s. political podcast channels, finding that many popular shows had a majority of episodes containing at least one toxic segment. while many of these studies have focused on youtube podcasts and estimating the ideology of youtube channels @xcite , there has yet to be a large-scale, data-driven analysis of the political bias in popular youtube podcast channels. what is rumble? launched in 2013 as a youtube alternative, rumble gained notable attention during the covid-19 pandemic @xcite . the number of monthly users on the platform increased from 1.6 million in the fall 2020 to 31.9 million by the beginning of 2021 @xcite and eventually hit a peak of 80 million active users monthly by the end of 2022 @xcite . while the platform's founder asserts its neutrality @xcite , rumble has become particularly known for being a haven for right-leaning public figures, including andrew tate, rudy giuliani, and alex jones @xcite . despite its popularity, research on this platform is limited. previous work @xcite estimated that over 75% of us adults who regularly use rumble for news are republicans or lean towards the republican party. this survey also notes that rumble is a regular news source for 2% of the american population. while rumble has been mentioned in research related to the alt-right and the russian invasion of ukraine @xcite , and some of andrew tate's rumble channel podcast episodes have undergone analysis @xcite , similar to youtube, a large-scale, data-driven research analyzing political bias in popular rumble podcast channels has yet to be conducted. ## dataset to collect podcast videos from rumble, we develop a custom crawler that extracts video information from the "podcasts" section on the home page of rumble.com @xcite . this crawler systematically navigates through the urls, scanning pages in this section until no new pages are found. we initially ran our crawler in october 2022 and conducted a follow-up in early 2023 to ensure coverage of the entire year. in the first week of july 2023, we revisited the video pages in our collection to update their metadata and remove any podcast videos that were no longer accessible. the rationale for this approach is to allow at least six full months for the metadata of each video (e.g., views) to stabilize and reflect their actual values. as a result, we compile a dataset of 6,761 videos from 246 channels, posted between august 27, 2020, to january 1, 2023. to remove non-english content from our dataset we perform language verification (see appendix a for details) and transcribe the podcast videos using a reimplementation of openai's whisper @xcite . a more comprehensive look at this dataset can be found in the corresponding dataset paper @xcite . as we aim to analyze popular rumble podcast channels, we limit our dataset to include the top 100 channels with the highest cumulative podcast video views. this subset comprises a total of 6,272 videos, accounting for 99% of all podcast views on rumble. youtube. using the youtube api, we extract video metadata categorized as podcasts from youtube's list of top 100 popular podcast creators (youtube, 2023). our manual inspection of these channels revealed non-english content and videos unrelated to podcasts (e.g., music and gospel). to refine our dataset, we used the following criteria: 1) videos must be categorized under the podcast tab within the channel's playlists, 2) the content must be in english, and 3) genres unrelated to podcasts (e.g., gospel and music) are excluded. in the refinement process, we randomly select and manually inspect 5 videos from each playlist, subsequently eliminating playlists that failed to meet our criteria. this process yields a dataset of more than 20k videos from 69 channels, with all videos available and their metadata collected during the first week of july 2023. for a comparative analysis with the rumble dataset, we adjust the youtube dataset to match the monthly video distribution and the total number of podcast videos in the rumble dataset. this way, by aligning the dataset with the specific months, we account for the potential influence of simultaneous events on the focus and content of the discussions. next, we eliminated non-english content following the methodologies outlined in appendix a. overall, we collect 6,272 podcast videos using youtube-dl @xcite . ## discussion & conclusion in this paper, we present the first large-scale datadriven study on podcast videos, where we analyzed the audio-visual content of popular rumble and youtube podcast channels, focusing on their political leanings. we present a methodology that can use multimodalilties for understanding video podcast content. our analysis of over 13k podcast videos demonstrates a right-wing bias in rumble's content, which sharply contrasts with youtube's more apolitical content. this dichotomy highlights the role of platforms in either reinforcing or challenging existing political narratives. our findings suggest that rumble's video podcast content is predominantly right-wing content, potentially creating a distinct echo chamber effect @xcite . this phenomenon is critical to understand, as it potentially exacerbates societal polarization in a yet underexplored area, e.g., podcasts. our findings also emphasize the need to consider both audio and visual elements in media studies. while textual content has been extensively analyzed in social media research, through this work, we emphasize the need to consider both audio and visual content when studying podcast videos, as cues from both modalities can be useful for understanding political leanings. furthermore, our study makes a valuable contribution to digital humanities by demonstrating how a multimodal, computational data analysis pipeline can deepen our understanding of cultural and political narratives in digital media. by integrating advanced speech-to-text transcription, transformer-based topic modeling, and visual content analysis, our approach bridges computational methods with humanistic inquiry. this methodological innovation not only expands the digital humanities toolkit but also provides a blueprint for exploring how audio, visual, and textual cues collectively shape public discourse and societal ideologies in emerging digital platforms.