Measuring musical preferences from listening behavior: Data from one million people and 200,000 songs

Abstract

Musical preferences are a fundamental individual difference predicting a multitude of listening behaviors. For decades, researchers have investigated how musical preferences are organized but have been hindered by genre-based and self-report methodologies. Recently, researchers have begun to investigate musical preferences at the feature-level using stimuli, rather than at the genre-level using self-reports. However, these new methods have been experimental and limited in their ecological validity. To address these recent limitations, we use an ecologically valid behavioral approach based on one million people who listened to more than 200,000 songs from streaming services, which is to our knowledge the largest study to date on the structure of musical preferences. Individual musical preference was measured from song playback counts and analyzed using principal components analysis on the psychological and sonic music features. Our results showed that music-feature preferences had a three-dimensional structure confirming previous theory and research. These dimensions are Arousal (level of energy in music), Valence (spectrum of negative to positive emotions), and Depth (intellectual and emotional depth in music). These findings lay firm ground for future research on music-feature preferences and pave the way for social-psychological and neurobiological experiments with music.