Diurnal fluctuations in musical preference

Heggli, Ole Adrian; Stupacher, Jan; Vuust, Peter

doi:10.1098/rsos.210885

Cited by 15 publications

(23 citation statements)

References 56 publications

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“…That said, other approaches in and outside of Twitter have been tested as well, producing complementary and in some cases alternative results. In one instance, online data exploration identified diurnal variations in musical preference on the streaming app, Spotify, where users showed significant preferences for low-valence (more 'sad, depressed') tracks during the night and early morning hours between 11pm and 6am (Heggli et al, 2021). These time trends match the general time trends uncovered for mood among Lumosity and Twitter users, and for suicide posts to Reddit.…”

Section: Discussionmentioning

confidence: 67%

Time-of-Day Variation in Subjective Mood Among Lumosity Users

Mendoza

Fernandez

Tubbs

et al. 2022

Preprint

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Human mood varies according to both endogenous and situational factors. Analyzing patterns of online media engagement can provide additional insights into a third dimension of mood potentially involving time-of-day factors. Using data collected through the online brain-training app, Lumosity, we report real-world trends in mood expression among 500,000 respondents. Endorsements of positive mood by Lumosity gamers peaked midday, while endorsements of negative mood peaked overnight. These data suggest that there may be inherent bias towards negative mood when individuals are awake at night. More broadly, they suggest that internet data-trails have the potential to uncover fundamental aspects of human behavior.

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Section: Discussionmentioning

confidence: 67%

Time-of-Day Variation in Subjective Mood Among Lumosity Users

Mendoza

Fernandez

Tubbs

et al. 2022

Preprint

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“…Out of these services, Spotify stands out with over 320 million listeners worldwide in 2020 [ 24 , 25 ]. In addition, Spotify offers an easily accessible API (application programming interface), allowing users and researchers to pull metadata and pre-calculated audio features from millions of unique tracks [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

The audio features of sleep music: Universal and subgroup characteristics

et al. 2023

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Throughout history, lullabies have been used to help children sleep, and today, with the increasing accessibility of recorded music, many people report listening to music as a tool to improve sleep. Nevertheless, we know very little about this common human habit. In this study, we elucidated the characteristics of music associated with sleep by extracting audio features from a large number of tracks (N = 225,626) retrieved from sleep playlists at the global streaming platform Spotify. Compared to music in general, we found that sleep music was softer and slower; it was more often instrumental (i.e. without lyrics) and played on acoustic instruments. Yet, a large amount of variation was present in sleep music, which clustered into six distinct subgroups. Strikingly, three of the subgroups included popular tracks that were faster, louder, and more energetic than average sleep music. The findings reveal previously unknown aspects of the audio features of sleep music and highlight the individual variation in the choice of music used for sleep. By using digital traces, we were able to determine the universal and subgroup characteristics of sleep music in a unique, global dataset, advancing our understanding of how humans use music to regulate their behaviour in everyday life.

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“…This approach has revealed that such playlists do indeed vary in terms of their Spotify audio features [ 27 ]. Using Spotify data, recent research demonstrated that people’s musical preferences follow a certain pattern depending on the period of the day [ 28 , 29 ]. In other words, Spotify audio features of music that people listen to fluctuate throughout the diurnal cycle.…”

Section: Introductionmentioning

confidence: 99%

“…In other words, Spotify audio features of music that people listen to fluctuate throughout the diurnal cycle. Musical preferences are characterized in the morning by high loudness, valence and energy, in the afternoon by an increase in tempo, beat strength and danceability, and during the night by the lowest values of loudness and tempo [ 28 ]. What’s more, a moderately significant correlation between diversity of human activity during the day and variability in the average Spotify audio features has been identified [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

“…Musical preferences are characterized in the morning by high loudness, valence and energy, in the afternoon by an increase in tempo, beat strength and danceability, and during the night by the lowest values of loudness and tempo [ 28 ]. What’s more, a moderately significant correlation between diversity of human activity during the day and variability in the average Spotify audio features has been identified [ 28 ]. This suggests that Spotify audio features of music people choose to listen to vary depending on the activity they engage with.…”

Section: Introductionmentioning

confidence: 99%

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Music we move to: Spotify audio features and reasons for listening

Duman

Neto²,

Mavrolampados³

et al. 2022

PLoS ONE

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Previous literature has shown that music preferences (and thus preferred musical features) differ depending on the listening context and reasons for listening (RL). Yet, to our knowledge no research has investigated how features of music that people dance or move to relate to particular RL. Consequently, in two online surveys, participants (N = 173) were asked to name songs they move to (“dance music”). Additionally, participants (N = 105) from Survey 1 provided RL for their selected songs. To investigate relationships between the two, we first extracted audio features from dance music using the Spotify API and compared those features with a baseline dataset that is considered to represent music in general. Analyses revealed that, compared to the baseline, the dance music dataset had significantly higher levels of energy, danceability, valence, and loudness, and lower speechiness, instrumentalness and acousticness. Second, to identify potential subgroups of dance music, a cluster analysis was performed on its Spotify audio features. Results of this cluster analysis suggested five subgroups of dance music with varying combinations of Spotify audio features: “fast-lyrical”, “sad-instrumental”, “soft-acoustic”, “sad-energy”, and “happy-energy”. Third, a factor analysis revealed three main RL categories: “achieving self-awareness”, “regulation of arousal and mood”, and “expression of social relatedness”. Finally, we identified variations in people’s RL ratings for each subgroup of dance music. This suggests that certain characteristics of dance music are more suitable for listeners’ particular RL, which shape their music preferences. Importantly, the highest-rated RL items for dance music belonged to the “regulation of mood and arousal” category. This might be interpreted as the main function of dance music. We hope that future research will elaborate on connections between musical qualities of dance music and particular music listening functions.

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Diurnal fluctuations in musical preference

Cited by 15 publications

References 56 publications

Time-of-Day Variation in Subjective Mood Among Lumosity Users

Time-of-Day Variation in Subjective Mood Among Lumosity Users

The audio features of sleep music: Universal and subgroup characteristics

Music we move to: Spotify audio features and reasons for listening

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