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

Nature Human Behaviour (2019) | Download Citation

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.

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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.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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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

Affiliations

  1. Department of Information Science, Cornell University, Ithaca, NY, USA

    • Minsu Park
    •  & Michael Macy
  2. Spotify USA, Somerville, MA, USA

    • Jennifer Thom
    • , Sarah Mennicken
    •  & Henriette Cramer
  3. Department of Sociology, Cornell University, Ithaca, NY, USA

    • Michael Macy

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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.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Minsu Park or Michael Macy.

Supplementary information

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    Supplementary Results, Supplementary Note, Supplementary References, Supplementary Figures 1–7, and Supplementary Tables 1–4

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DOI

https://doi.org/10.1038/s41562-018-0508-z