Abstract
People manage emotions to cope with life’s demands1,2. Previous research has identified affective patterns using self-reports3 and text analysis4,5, but these measures track the expression of affect, not affective preference for external stimuli such as music, which affects mood states and levels of emotional arousal1,6,7. We analysed a dataset of 765 million online music plays streamed by 1 million individuals in 51 countries to measure diurnal and seasonal patterns of affective preference. Findings reveal similar diurnal patterns across cultures and demographic groups. Individuals listen to more relaxing music late at night and more energetic music during normal business hours, including mid-afternoon when affective expression is lowest. However, there were differences in baselines: younger people listen to more intense music; compared with other regions, music played in Latin America is more arousing, while music in Asia is more relaxing; and compared with other chronotypes, ‘night owls’ (people who are habitually active or wakeful at night) listen to less-intense music. Seasonal patterns vary with distance from the equator and between Northern and Southern hemispheres and are more strongly correlated with absolute day length than with changes in day length. Taken together with previous findings on affective expression in text4, these results suggest that musical choice both shapes and reflects mood.
This is a preview of subscription content, access via your institution
Access options
Similar content being viewed by others
Code availability
Aggregate data and code are available at https://github.com/minsu-park/affective_preference_rhythm.
Data availability
The datasets used in this study are available from Spotify, but restrictions apply to the availability of these data, which were used under an agreement for the current study, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission from Spotify.
References
Thayer, R. E., Newman, J. R. & McClain, T. M. Self-regulation of mood: strategies for changing a bad mood, raising energy, and reducing tension. J. Pers. Soc. Psychol. 67, 910–925 (1994).
Gross, J. J. The emerging field of emotion regulation: an integrative review. Rev. Gen. Psychol. 2, 271–299 (1998).
Clark, L. A., Watson, D. & Leeka, J. Diurnal variation in the positive affects. Motiv. Emot. 13, 205–234 (1989).
Golder, S. A. & Macy, M. W. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333, 1878–1881 (2011).
Dodds, P. S. & Danforth, C. M. Measuring the happiness of large-scale written expression: songs, blogs, and presidents. J. Happiness Stud. 11, 441–456 (2010).
Hargreaves, D. J. & North, A. C. The functions of music in everyday life: redefining the social in music psychology. Psychol. Music 27, 71–83 (1999).
Knobloch, S. & Zillmann, D. Mood management via the digital jukebox. J. Commun. 52, 351–366 (2002).
Gross, J. J. & John, O. P. Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. J. Pers. Soc. Psychol. 85, 348–362 (2003).
Zillmann, D. Mood management: using entertainment to full advantage. Commun. Soc. Cogn. Affect. 31, 147–171 (1988).
DeNora, T. Music in Everyday Life (Cambridge Univ. Press, Cambridge, UK, 2000).
Sloboda, J. A. & O’Neill, S. A. in Music and Emotion: Theory and Research 415–429 (Oxford Univ. Press, Oxford, UK, 2001).
Thayer, R. E. The Origin of Everyday Moods: Managing Energy, Tension, and Stress (Oxford Univ. Press, Oxford, UK, 1997).
North, A. C., Hargreaves, D. J. & Hargreaves, J. J. Uses of music in everyday life. Music Percept. 22, 41–77 (2004).
Greenberg, D. M. & Rentfrow, P. J. Music and big data: a new frontier. Curr. Opin. Behav. Sci. 18, 50–56 (2017).
Ipsos Connect. Music Consumer Insight Report https://www.ifpi.org/downloads/Music-Consumer-Insight-Report-2016.pdf (International Federation of the Phonographic Industry, 2016).
U.S. Smartphone Use (Pew Research Center, 2016).
Communications Market Report 2017: United Kingdom (Ofcom, 2017).
Mislove, A., Lehmann, S., Ahn, Y.-Y., Onnela, J.-P. & Rosenquist, J. N. Pulse of the Nation: U.S. Mood Throughout the Day Inferred from Twitter Khoury College of Computer and Information Sciences, Northeastern University http://www.ccs.neu.edu/home/amislove/twittermood/ (2010).
Thelwall, M., Buckley, K. & Paltoglou, G. Sentiment in Twitter events. J. Assoc. Inf. Sci. Technol. 62, 406–418 (2011).
Kramer, A. D. I., Guillory, J. E. & Hancock, J. T. Experimental evidence of massive-scale emotional contagion through social networks. Proc. Natl Acad. Sci. USA 111, 8788–8790 (2014).
Samuelson, P. Consumption theory in terms of revealed preference. Economica 15, 243–253 (1948).
The World Factbook (Central Intelligence Agency, 2017).
Tsai, J. L. Ideal affect: cultural causes and behavioral consequences. Perspect. Psychol. Sci. 2, 242–259 (2007).
Keller, M. C. et al. A warm heart and a clear head: the contingent effects of weather on mood and cognition. Psychol. Sci. 16, 724–731 (2005).
Pettijohn, T. F., Williams, G. M. & Carter, T. C. Music for the seasons: seasonal music preferences in college students. Curr. Psychol. 29, 328–345 (2010).
Krause, A. E. & North, A. C. ’Tis the season: music-playlist preferences for the seasons. Psychol. Aesthet. Creat. Arts 12, 89–95 (2018).
Harmatz, M. G. et al. Seasonal variation of depression and other moods: a longitudinal approach. J. Biol. Rhythms 15, 344–350 (2000).
Spotify Technology S.A. Direct Listing Prospectus (Spotify, 2018).
Balkwill, L.-L. & Thompson, W. F. A cross-cultural investigation of the perception of emotion in music: psychophysical and cultural cues. Music Percept. 17, 43–64 (1999).
Askin, N. & Mauskapf, M. What makes popular culture popular? Product features and optimal differentiation in music. Am. Sociol. Rev. 82, 910–944 (2017).
Greenberg, D. M. et al. The song is you: preferences for musical attribute dimensions reflect personality. Soc. Psychol. Pers. Sci. 7, 597–605 (2016).
Forsythe, W. C., Rykiel, E. J., Stahl, R. S., Wu, H. & Schoolfield, R. M. A model comparison for daylength as a function of latitude and day of year. Ecol. Model. 80, 87–95 (1995).
Brock, T. D. Calculating solar radiation for ecological studies. Ecol. Model. 14, 1–19 (1981).
Fricke, K. R., Greenberg, D. M., Rentfrow, P. J. & Herzberg, P. Y. Computer-based music feature analysis mirrors human perception and can be used to measure individual music preference. J. Res. Pers. 75, 94–102 (2018).
Thistlethwaite, D. L. & Campbell, D. T. Regression-discontinuity analysis: an alternative to the ex post facto experiment. J. Educ. Psychol. 51, 309–317 (1960).
Imbens, G. & Kalyanaraman, K. Optimal bandwidth choice for the regression discontinuity estimator. Rev. Econ. Stud. 79, 933–959 (2012).
Gelman, A. & Imbens, G. Why high-order polynomials should not be used in regression discontinuity designs. J. Bus. Econ. Stat. https://doi.org/10.1080/07350015.2017.1366909 (2018).
McCrary, J. Manipulation of the running variable in the regression discontinuity design: a density test. J. Econometrics 142, 698–714 (2008).
Acknowledgements
This research and data analysis were predominantly carried out during M.P.’s internship at Spotify. We thank M. Antoniak, F. Diaz, T. Y. Hou, J. Park, J. Zhang, members of Cornell’s Social Dynamics Laboratory, and colleagues at Spotify for helpful suggestions. This research was supported by the US National Science Foundation (SES 1226483), Minerva Initiative (FA9550-15-1-0162) and the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2016S1A3A2925033). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Author information
Authors and Affiliations
Contributions
M.P. and M.M. designed the research, analysed the results, and wrote the first draft. M.P. conducted the analyses. M.P., J.T., S.M., H.C., and M.M. jointly wrote the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Supplementary Information
Supplementary Results, Supplementary Note, Supplementary References, Supplementary Figures 1–7, and Supplementary Tables 1–4
Rights and permissions
About this article
Cite this article
Park, M., Thom, J., Mennicken, S. et al. Global music streaming data reveal diurnal and seasonal patterns of affective preference. Nat Hum Behav 3, 230–236 (2019). https://doi.org/10.1038/s41562-018-0508-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41562-018-0508-z
This article is cited by
-
Music preferences as an instrument of emotional self-regulation along the business cycle
Journal of Cultural Economics (2023)
-
Musical preference in an online music community in China
Social Network Analysis and Mining (2022)
-
Daily, weekly, seasonal and menstrual cycles in women’s mood, behaviour and vital signs
Nature Human Behaviour (2021)
-
Elites, communities and the limited benefits of mentorship in electronic music
Scientific Reports (2020)