Mostra: A Flexible Balancing Framework to Trade-off User, Artist and Platform Objectives for Music Sequencing
Emanuele Bugliarello, Rishabh Mehrotra, James Kirk, Mounia Lalmas
While there is an abundance of advice 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 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 pieces of advice and adding new perspectives on others.
Emanuele Bugliarello, Rishabh Mehrotra, James Kirk, Mounia Lalmas
Maryam Aziz, Alice Wang, Aasish Pappu, Hugues Bouchard,Yu Zhao, Benjamin Carterette and Mounia Lalmas
Sravana Reddy, Yongze Yu, Aasish Pappu, Aswin Sivaraman, Rezvaneh Rezapour, Rosie Jones