iDRAMA-rumble-2024: A Dataset of Podcasts from Rumble Spanning 2020 to 2022
Description
ABSTRACT---------------Rumble has emerged as a prominent platform hosting controversial figures facing restrictions on YouTube. Despite this, the academic community’s engagement with Rumble has been minimal. To help researchers address this gap, we introduce a comprehensive dataset of about 6.7K podcast videos from August 2020 to December 2022, amounting to over 5.6K hours of content. Besides covering metadata of these podcast videos, we provide speech-to-text transcriptions for future analysis. We also provide speaker diarization information, a collection of ~250K unique representative images from podcast videos, and face embeddings of ~400K extracted faces. With the rise of the influence of podcasts and populist figures, this dataset provides a rich resource for identifying challenges in cyber social threats in a relatively underexplored space.Rumble platform: http://rumble.com/Link to paper: https://workshop-proceedings.icwsm.org/abstract.php?id=2024_07License: CC BY-NC-SA 4.0Dataset SummaryiDRAMA-rumble-2024 is a large-scale dataset of 6,735 podcast videos from Rumble, an alternative Youtube-like platform. Using state-of-the-art models, we extract information across three modalities: 1) text, 2) audio, and 3) video. We detail the methodology for extracting information from podcast videos in the paper and release a first-of-its-kind dataset including data from different modalities:Metadata: Details about podcast videos, e.g., channel name, video name, video description, and more.Text: Transcription (i.e., speech-to-text) of podcast videos.Audio: Speaker diarization information providing speaker detection over time for each video.Video: Sampled representative video frames from each video, totaling 200K images. We also detect ~400K non-unique faces from these images and release face embeddings.Repository linksZenodo: On Zenodo, we provide JSON formatted dataset for all modalities and representative images in compressed files.Github: The main repository of this dataset, where we provide code snippets to get started with this dataset.Link here: https://github.com/idramalab/iDRAMA-rumble-2024Huggingface: On Huggingface, we provide a dataset that can be accessed through Huggingface APIs in a parquet format.Link here: https://hf.co/datasets/iDRAMALab/iDRAMA-rumble-2024Dataset InfoThe dataset is organized by modalities -- transcripts, representative images, speaker diarization, and face embeddings.ConfigData-pointsPodcast videos6,735Representative images252,387Face embeddings399,333Transcripts & Speaker diarization6,735Zenodo Dataset Files Info #FilesFile namesMetadata1iDRAMA-rumble-2024-metadata.ndjsonSpeaker diarization1iDRAMA-rumble-2024-speaker-dirization.zipFace embeddings1iDRAMA-rumble-2024-face-embeddings.ndjsonRepresentation images5iDRAMA-rumble-2024-repr-images-set1.tar.gziDRAMA-rumble-2024-repr-images-set2.tar.gziDRAMA-rumble-2024-repr-images-set3.tar.gziDRAMA-rumble-2024-repr-images-set4.tar.gziDRAMA-rumble-2024-repr-images-set5.tar.gzTranscription Lite(Minimal information)3iDRAMA-rumble-2024-transcription-lite_part_1.ndjsoniDRAMA-rumble-2024-transcription-lite_part_2.ndjsoniDRAMA-rumble-2024-transcription-lite_part_3.ndjsonTranscription3iDRAMA-rumble-2024-transcription_part_1.ndjsoniDRAMA-rumble-2024-transcription_part_2.ndjsoniDRAMA-rumble-2024-transcription_part_3.ndjsonAuthorshipThis dataset is published in the "Workshop Proceedings of the 18th International AAAI Conference on Web and Social Media" hosted in Buffalo, NY, USA.Academic Organization: iDRAMA LabAuthors: Utkucan Balci, Jay Patel, Berkan Balci, Jeremy BlackburnAffiliation: Binghamton University, Middle East Technical UniversityLicensingThis dataset is available for free to use under terms of the non-commercial license CC BY-NC-SA 4.0.Citation@article{balci2024idrama, title = {iDRAMA-rumble-2024: A Dataset of Podcasts from Rumble Spanning 2020 to 2022}, author = {Balci, Utkucan and Patel, Jay and Balci, Berkan and Blackburn, Jeremy}, year = {2024}, journal = {Workshop Proceedings of the 18th International AAAI Conference on Web and Social Media}}
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Publication Details
DOI
Publisher
Zenodo
Subfield
Sociology and Political Science
Field
Social Sciences
Domain
Social Sciences
Confidence Score
40%
Source
Scholar Data Model