Influence of gender dimorphism on audience engagement in podcasts: a machine learning analysis of dynamic affective linguistic and paralinguistic features

Effective communication is a crucial objective for business leaders, educators, and politicians alike. Achieving impactful communication involves not only the selection of appropriate words but also proficiency in their delivery. Previous research has frequently examined linguistic, affective linguistic, and paralinguistic features in isolation, thereby overlooking their cumulative impact over time. This study addresses this gap by utilizing a machine learning approach to analyze the dynamic interplay between affective linguistic and paralinguistic features across various episodes of online podcasts. Furthermore, this research incorporates an analysis of gender disparities, acknowledging the dimorphic nature of language and speech across genders. Our findings suggest that accounting for gender when examining the dynamic interactions between affective linguistic and paralinguistic features over time, known as emotional volatility, significantly improves the explanatory power of variations in audience engagement compared to analyses that consider these variables separately.