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Global music streaming data reveal diurnal and seasonal patterns of affective preference


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.

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Fig. 1: Millions of global music plays reveal diurnal affective patterns.
Fig. 2: Diurnal affective patterns are robust across diverse geographic regions, demographic groups and chronotypes.
Fig. 3: Affective preference is associated with seasonal variation in day length.

Code availability

Aggregate data and code are available at

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.


  1. 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).

    Article  CAS  Google Scholar 

  2. Gross, J. J. The emerging field of emotion regulation: an integrative review. Rev. Gen. Psychol. 2, 271–299 (1998).

    Article  Google Scholar 

  3. Clark, L. A., Watson, D. & Leeka, J. Diurnal variation in the positive affects. Motiv. Emot. 13, 205–234 (1989).

    Article  Google Scholar 

  4. Golder, S. A. & Macy, M. W. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333, 1878–1881 (2011).

    Article  CAS  Google Scholar 

  5. 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).

    Article  Google Scholar 

  6. 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).

    Article  Google Scholar 

  7. Knobloch, S. & Zillmann, D. Mood management via the digital jukebox. J. Commun. 52, 351–366 (2002).

    Article  Google Scholar 

  8. 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).

    Article  Google Scholar 

  9. Zillmann, D. Mood management: using entertainment to full advantage. Commun. Soc. Cogn. Affect. 31, 147–171 (1988).

    Google Scholar 

  10. DeNora, T. Music in Everyday Life (Cambridge Univ. Press, Cambridge, UK, 2000).

  11. Sloboda, J. A. & O’Neill, S. A. in Music and Emotion: Theory and Research 415–429 (Oxford Univ. Press, Oxford, UK, 2001).

  12. Thayer, R. E. The Origin of Everyday Moods: Managing Energy, Tension, and Stress (Oxford Univ. Press, Oxford, UK, 1997).

  13. North, A. C., Hargreaves, D. J. & Hargreaves, J. J. Uses of music in everyday life. Music Percept. 22, 41–77 (2004).

    Article  Google Scholar 

  14. Greenberg, D. M. & Rentfrow, P. J. Music and big data: a new frontier. Curr. Opin. Behav. Sci. 18, 50–56 (2017).

    Article  Google Scholar 

  15. Ipsos Connect. Music Consumer Insight Report (International Federation of the Phonographic Industry, 2016).

  16. U.S. Smartphone Use (Pew Research Center, 2016).

  17. Communications Market Report 2017: United Kingdom (Ofcom, 2017).

  18. 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 (2010).

  19. Thelwall, M., Buckley, K. & Paltoglou, G. Sentiment in Twitter events. J. Assoc. Inf. Sci. Technol. 62, 406–418 (2011).

    Article  Google Scholar 

  20. 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).

    Article  CAS  Google Scholar 

  21. Samuelson, P. Consumption theory in terms of revealed preference. Economica 15, 243–253 (1948).

    Article  Google Scholar 

  22. The World Factbook (Central Intelligence Agency, 2017).

  23. Tsai, J. L. Ideal affect: cultural causes and behavioral consequences. Perspect. Psychol. Sci. 2, 242–259 (2007).

    Article  Google Scholar 

  24. 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).

    Article  Google Scholar 

  25. 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).

    Article  Google Scholar 

  26. Krause, A. E. & North, A. C. ’Tis the season: music-playlist preferences for the seasons. Psychol. Aesthet. Creat. Arts 12, 89–95 (2018).

    Article  Google Scholar 

  27. Harmatz, M. G. et al. Seasonal variation of depression and other moods: a longitudinal approach. J. Biol. Rhythms 15, 344–350 (2000).

    Article  CAS  Google Scholar 

  28. Spotify Technology S.A. Direct Listing Prospectus (Spotify, 2018).

  29. 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).

    Article  Google Scholar 

  30. Askin, N. & Mauskapf, M. What makes popular culture popular? Product features and optimal differentiation in music. Am. Sociol. Rev. 82, 910–944 (2017).

    Article  Google Scholar 

  31. Greenberg, D. M. et al. The song is you: preferences for musical attribute dimensions reflect personality. Soc. Psychol. Pers. Sci. 7, 597–605 (2016).

    Article  Google Scholar 

  32. 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).

    Article  Google Scholar 

  33. Brock, T. D. Calculating solar radiation for ecological studies. Ecol. Model. 14, 1–19 (1981).

    Article  Google Scholar 

  34. 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).

    Article  Google Scholar 

  35. Thistlethwaite, D. L. & Campbell, D. T. Regression-discontinuity analysis: an alternative to the ex post facto experiment. J. Educ. Psychol. 51, 309–317 (1960).

    Article  Google Scholar 

  36. Imbens, G. & Kalyanaraman, K. Optimal bandwidth choice for the regression discontinuity estimator. Rev. Econ. Stud. 79, 933–959 (2012).

    Article  Google Scholar 

  37. Gelman, A. & Imbens, G. Why high-order polynomials should not be used in regression discontinuity designs. J. Bus. Econ. Stat. (2018).

  38. McCrary, J. Manipulation of the running variable in the regression discontinuity design: a density test. J. Econometrics 142, 698–714 (2008).

    Article  Google Scholar 

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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.

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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.

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Correspondence to Minsu Park or Michael Macy.

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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).

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