Psychologists have long theorized that people use music to create auditory environments matching their personality traits. While there is initial evidence relating self-reported musical style preferences to the Big Five dimensions, little is known about day-to-day music listening behavior and the intrinsic attributes of music that give rise to personality patterns. The present study (N = 330) proposes a personality computing approach to fill these gaps with new insights from ecologically valid music listening records from smartphones. We provided a holistic account of individual differences in music listening by integrating everyday preferences for various musical attributes with habitual listening behaviors. More specifically, we quantified music preferences at fine granularity via technical audio features from Spotify and via lyrical attributes obtained through natural language processing. Using machine learning algorithms, these behavioral variables served to predict Big Five personality on domain and facet level. Our out-of-sample prediction accuracies revealed that the Openness dimension was most strongly related to music listening, while several other traits (most notably Conscientiousness facets) also showed moderate effects. Thereby, preferences for audio and lyrics characteristics were distinctly predictive of personality, hinting at the incremental value of both musical components. Furthermore, variable importance metrics displayed generally trait-congruent relationships between personality outcomes and music listening behaviors prompting us to discuss possible mechanisms underlying these interactions. Overall, our study contributes to the development of a detailed cumulative theory on music listening in personality science, which may be extended in numerous ways in future studies leveraging the computational framework proposed here.
It is a long-held belief in psychology and beyond that individuals’ music preferences reveal information about their personality traits. While initial evidence relates self-reported preferences for broad musical styles to the Big Five dimensions, little is known about day-to-day music listening behavior and the intrinsic attributes of melodies and lyrics that reflect these individual differences. The present study (N = 330) proposes a personality computing approach to fill these gaps with new insights from ecologically valid music listening records from smartphones. We quantified participants’ music preferences via audio and lyrics characteristics of their played songs through technical audio features from Spotify and textual attributes obtained via natural language processing. Using linear elastic net and non-linear random forest models, these behavioral variables served to predict Big Five personality on domain and facet levels. Out-of-sample prediction performances revealed that – on the domain level – Openness was most strongly related to music listening (r = .25), followed by Conscientiousness (r = .13), while several facets of the Big Five also showed small to medium effects. Hinting at the incremental value of audio and lyrics characteristics, both musical components were differentially informative for models predicting Openness and its facets, whereas lyrics preferences played the more important role for predictions of Conscientiousness dimensions. In doing so, the models’ most predictive variables displayed generally trait-congruent relationships between personality and music preferences. These findings contribute to the development of a cumulative theory on music listening in personality science and may be extended in numerous ways by future work leveraging the computational framework proposed here.
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