Personality Computing With Naturalistic Music Listening Behavior: Comparing Audio and Lyrics Preferences

Abstract

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.

A Template and Tutorial for Preregistering Studies Using Passive Smartphone Measures

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From Embeddings to Explainability: A Tutorial on Transformer-Based Text Analysis for Social and Behavioral Scientists

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Best Practices in Supervised Machine Learning: A Tutorial for Psychologists

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