Abstract

Podcasts are an easily accessible medium of entertainment and information, often covering content from a variety of domains. However, only a few of them garner enough attention to be deemed ‘popular’. In this work, we investigate the textual cues that assist in differing popular podcasts from unpopular ones. Despite having very similar polarity and subjectivity, the lexical cues contained in the podcasts are significantly different. Thus, we employ a triplet-based training method, to learn a text-based representation of a podcast, which is then used for a downstream task of “popularity prediction”. Our best model received an F1 score of 0.82, achieving a relative improvement over the best baseline by 12.3%.

Date

September 4, 2025

Authors

Brihi Joshi, Shravika Mittal, Aditya Chetan

Conference

Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)

Pages

13-17