# Mapping the Podcast Ecosystem with the Structured Podcast Research Corpus

arXiv: https://arxiv.org/abs/2411.07892 · HTML: https://mlsanity.com/paper/2411.07892/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 67 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Builds on (most cited first, 8 of 8)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [POTATO: The Portable Text Annotation Tool](https://mlsanity.com/paper/2212.08620/) (2022)
- [Careless Whisper: Speech-to-Text Hallucination Harms](https://mlsanity.com/paper/2402.08021/) (2024)
- [Quantifying social organization and political polarization in online platforms](https://mlsanity.com/paper/2010.00590/) (2020)
- [Just Another Day on Twitter: A Complete 24 Hours of Twitter Data](https://mlsanity.com/paper/2301.11429/) (2023)
- [QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Patterns](https://mlsanity.com/paper/1504.01383/) (2015)
- [Political Discussion is Abundant in Non-political Subreddits (and Less Toxic)](https://mlsanity.com/paper/2104.09560/) (2021)
- [When it Rains, it Pours: Modeling Media Storms and the News Ecosystem](https://mlsanity.com/paper/2312.02118/) (2023)

## Built on by (most cited first, 6 of 6)

- [Summarizing Speech: A Comprehensive Survey](https://mlsanity.com/paper/2504.08024/) (2025)
- [Toxicity Begets Toxicity: Unraveling Conversational Chains in Political Podcasts](https://mlsanity.com/paper/2501.12640/) (2025)
- [When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms](https://mlsanity.com/paper/2604.16767/) (2026)
- [Context-Aware Multimodal Claim Verification in Spoken Dialogues](https://mlsanity.com/paper/2606.11420/) (2026)
- [Listening Between the Lines: Decoding Podcast Narratives with Language Modeling](https://mlsanity.com/paper/2511.05310/) (2025)
- [ESTS at WMT26: Routing-Informed Expert Pruning for Model Compression](https://mlsanity.com/paper/2609.12310/) (2026)

## Abstract (arXiv)

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.