Mapping the Podcast Ecosystem with the Structured Podcast Research Corpus
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914210821177344 |
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| author | Litterer, Benjamin Jurgens, David Card, Dallas |
| author_facet | Litterer, Benjamin Jurgens, David Card, Dallas |
| contents | Podcasts provide highly diverse content to a massive listener base through a unique on-demand modality. However, limited data has prevented large-scale computational analysis of the podcast ecosystem. To fill this gap, we introduce a massive dataset of over 1.1M podcast transcripts that is largely comprehensive of all English language podcasts available through public RSS feeds from May and June of 2020. This data is not limited to text, but rather includes audio features and speaker turns for a subset of 370K episodes, and speaker role inferences and other metadata for all 1.1M episodes. Using this data, we also conduct a foundational investigation into the content, structure, and responsiveness of this ecosystem. Together, our data and analyses open the door to continued computational research of this popular and impactful medium. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_07892 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mapping the Podcast Ecosystem with the Structured Podcast Research Corpus Litterer, Benjamin Jurgens, David Card, Dallas Computation and Language Computers and Society Podcasts provide highly diverse content to a massive listener base through a unique on-demand modality. However, limited data has prevented large-scale computational analysis of the podcast ecosystem. To fill this gap, we introduce a massive dataset of over 1.1M podcast transcripts that is largely comprehensive of all English language podcasts available through public RSS feeds from May and June of 2020. This data is not limited to text, but rather includes audio features and speaker turns for a subset of 370K episodes, and speaker role inferences and other metadata for all 1.1M episodes. Using this data, we also conduct a foundational investigation into the content, structure, and responsiveness of this ecosystem. Together, our data and analyses open the door to continued computational research of this popular and impactful medium. |
| title | Mapping the Podcast Ecosystem with the Structured Podcast Research Corpus |
| topic | Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2411.07892 |