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Ole Adrian Heggli1, Jan Stupacher1, Peter Vuust1

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Human daily rhythms influence music choice, dividing listening into five time blocks. Playlists reflect these diurnal patterns more than individual song selections.

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Area of Science:

  • Chronobiology
  • Music Psychology
  • Computational Social Science

Background:

  • Human behavior and affective states exhibit systematic diurnal patterns driven by circadian processes.
  • Population-level diurnal patterns in music preference have been previously observed.

Purpose of the Study:

  • To analyze large-scale music streaming data to identify diurnal patterns in music listening behavior.
  • To investigate whether music playlists, as opposed to individual tracks, reflect an awareness of these diurnal preferences.

Main Methods:

  • Analysis of over two billion music streaming events from Spotify, focusing on audio features.
  • Integration of an artificial neural network with the Spotify API to assess playlist characteristics.

Main Results:

  • Music listening behavior naturally divides into five distinct time blocks throughout the day (morning, afternoon, evening, night, late night/early morning).
  • Artificial neural networks reveal that music playlists demonstrate a general awareness of diurnal preferences, more so than individual tracks.

Conclusions:

  • Music listening is intertwined with daily life and influenced by underlying circadian rhythms.
  • Diurnal patterns significantly shape even seemingly individual preferences like music choice, particularly at the playlist level.