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Music listening is a pervasive behavior in everyday life that varies both inter- and intraindividually. While personality science has repeatedly related interindividual differences in overall music preferences (i.e., musical taste) to various stable personality traits (e.g., Anderson et al., 2021; Rentfrow & Gosling, 2003), intraindividual fluctuations in daily music-listening behaviors remain largely unaccounted for. Because music is commonly used to modify or maintain the occurrence, duration, and intensity of emotional experiences (e.g., DeNora, 1999; Juslin & Laukka, 2004; Schäfer et al., 2013), mood states may be an important predictor of these music-listening fluctuations. As mood regulation and music preferences are, in turn, differentially related to personality, the Big Five traits may also play a role in these mood-music associations. Hence, the present study will investigate the relationship between music-listening behaviors, fluctuating mood states, and stable personality traits. For this purpose, we will explore natural music listening behavior on the level of listening events collected via smartphone sensing. Thereby, we will represent music listening via listeners’ momentary choice of music in terms of technical audio features.

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We aim at predicting personality traits from natural music listening behavior logged from smartphones. This research project is part of the PhoneStudy project and re-uses data from those studies.

The purpose of this study is to answer the question whether smartphone sensing data can be used to predict subjective sleep quality on a day-to-day level. Subjective Sleep Quality was gathered via consecutive questionnaires in a experience sampling timeframe of a larger-scaled study. Smartphone Sensing data includes information on smartphone usage in general, usage of certain app categories (Social Media/Messaging, Gaming), data on physical activity generated via API, and data on ambient noise and light. These broad categories of relevant smartphone sensing data are derived from literature.

Using data from questionnaires and smartphone sensing, this study aims to examine whether the relationships of the personality trait Emotional Stability with preferred musical Valence and the personality trait Extraversion with preferred musical Instrumentalness are mediated by the extent to which music is used for emotional regulation. This research project reuses data from the PhoneStudy project (see https://osf.io/ut42y/).

Using smartphone data for the predictive modelling of coping strategies - benchmarking of machine learning algorithms based on smartphone sensing, experience sampling and survey data

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.