Mapping the Podcast Ecosystem with the Structured Podcast Research Corpus
This addresses the data scarcity problem for researchers studying the podcast ecosystem, enabling computational research on this popular medium, though it is incremental as it primarily provides a new dataset.
The authors tackled the lack of large-scale data for computational analysis of podcasts by introducing a dataset of over 1.1M English podcast transcripts from 2020, including audio features and metadata for subsets, and conducted foundational analyses on content and structure.
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.