# Modeling Language Usage and Listener Engagement in Podcasts

arXiv: https://arxiv.org/abs/2106.06605 · HTML: https://mlsanity.com/paper/2106.06605/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 50 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings](https://mlsanity.com/paper/1904.01596/) (2019)
- [Auditing Radicalization Pathways on YouTube](https://mlsanity.com/paper/1908.08313/) (2019)
- [The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter](https://mlsanity.com/paper/1405.1438/) (2014)
- [How algorithmic popularity bias hinders or promotes quality](https://mlsanity.com/paper/1707.00574/) (2017)
- [Automated Analysis and Prediction of Job Interview Performance](https://mlsanity.com/paper/1504.03425/) (2015)
- [Finding Your Voice: The Linguistic Development of Mental Health Counselors](https://mlsanity.com/paper/1906.07194/) (2019)
- [Detecting Extraneous Content in Podcasts](https://mlsanity.com/paper/2103.02585/) (2021)
- [Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books](https://mlsanity.com/paper/1805.09746/) (2018)
- [FairyTED: A Fair Rating Predictor for TED Talk Data](https://mlsanity.com/paper/1911.11558/) (2019)
- [Measuring Forecasting Skill from Text](https://mlsanity.com/paper/2006.07425/) (2020)
- [The Engagement-Diversity Connection: Evidence from a Field Experiment on Spotify](https://mlsanity.com/paper/2003.08203/) (2020)
- [Mutual Hyperlinking Among Misinformation Peddlers](https://mlsanity.com/paper/2104.11694/) (2021)
- [A review of metadata fields associated with podcast RSS feeds](https://mlsanity.com/paper/2009.12298/) (2020)
- [A Readability Analysis of Campaign Speeches from the 2016 US Presidential Campaign](https://mlsanity.com/paper/1603.05739/) (2016)
- [Identifying the sentiment styles of YouTube's vloggers](https://mlsanity.com/paper/1808.09722/) (2018)

## Built on by (most cited first, 2 of 2)

- [MedNgage: A Dataset for Understanding Engagement in Patient-Nurse Conversations](https://mlsanity.com/paper/2305.19981/) (2023)
- [Prediction of Listener Perception of Argumentative Speech in a Crowdsourced Dataset Using (Psycho-)Linguistic and Fluency Features](https://mlsanity.com/paper/2111.07130/) (2021)

## Abstract (arXiv)

While there is an abundance of popular writing targeted to podcast creators on how to speak in ways that engage their listeners, there has been little data-driven analysis of podcasts that relates linguistic style with listener engagement. In this paper, we investigate how various factors -- vocabulary diversity, distinctiveness, emotion, and syntax, among others -- correlate with engagement, based on analysis of the creators' written descriptions and transcripts of the audio. We build models with different textual representations, and show that the identified features are highly predictive of engagement. Our analysis tests popular wisdom about stylistic elements in high-engagement podcasts, corroborating some aspects, and adding new perspectives on others.