“Evacuate the dancefloor”: Exploring and classifying spotify music listening before and during the COVID-19 pandemic in DACH countries

Kalustian, Kework K.; Ruth, Nicolas

doi:10.5964/jbdgm.95

Cited by 8 publications

(3 citation statements)

References 55 publications

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“…An alternative kind of explainable feature selection is proposed by [84] by initializing a weighted graph to comprise features with Pearson similarity criteria for the feature similarities calculation as well as the integration of Fisher score (FS) and the node centrality to determine the score of each feature. That way, the feature selection approach considers not only feature importance but also feature similarity.…”

Section: Feature Selectionmentioning

confidence: 99%

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

Khalili,

Wimmer

2024

Life

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By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control of the spread of the SARS-CoV-2 virus. Along with this, epidemiological machine learning studies of SARS-CoV-2 have been frequently published. While these models can be perceived as precise and policy-relevant to guide governments towards optimal containment policies, their black box nature can hamper building trust and relying confidently on the prescriptions proposed. This paper focuses on interpretable AI-based epidemiological models in the context of the recent SARS-CoV-2 pandemic. We systematically review existing studies, which jointly incorporate AI, SARS-CoV-2 epidemiology, and explainable AI approaches (XAI). First, we propose a conceptual framework by synthesizing the main methodological features of the existing AI pipelines of SARS-CoV-2. Upon the proposed conceptual framework and by analyzing the selected epidemiological studies, we reflect on current research gaps in epidemiological AI toolboxes and how to fill these gaps to generate enhanced policy support in the next potential pandemic.

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Section: Feature Selectionmentioning

confidence: 99%

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

Khalili,

Wimmer

2024

Life

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“…This surprising finding was interpreted as a possible explanation for various motivations for listening to music. Other work has linked musical preferences with stress management and emotional coping strategies during the COVID-19 pandemic [ 31 , 32 ] and with patients’ pain management [ 33 ]. In particular, Spotify audio features such as high valence, energy, danceability, tempo and low instrumentalness were found to correlate with the music people choose associated with emotional coping mechanisms.…”

Section: Introductionmentioning

confidence: 99%

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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“…This surprising finding was interpreted as a possible explanation for various motivations for listening to music. Other work has linked musical preferences with stress management and emotional coping strategies during the COVID-19 pandemic [31,32] and with patients' pain management [33]. In particular, Spotify audio features such as high valance, energy, danceability, tempo and low instrumentalness were found to correlate with the music people choose associated with emotional coping mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Music we move to: audio features and reasons for listening

Duman¹,

Neto²,

Mavrolampados³

et al. 2022

Preprint

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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, 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’, or DM). A subset of the same participants (N=105) additionally provided RL for their selected songs. To investigate relationships between the two, we first extracted audio features from DM using the Spotify API and compared those features with a baseline dataset representative of music in general (‘general music’, or GM). Analysis revealed that, compared to GM, DM has significantly higher levels of energy, danceability, valence, and loudness, and lower speechiness, instrumentalness and acousticness. Second, to identify potential subgroups of DM, a cluster analysis was performed on its audio features. Results of this cluster analysis suggested five subgroups of DM with varying combinations of 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 DM. This suggests that certain characteristics of DM 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 DM. We hope that future research will elaborate on connections between musical qualities of DM and particular music listening functions.

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“Evacuate the dancefloor”: Exploring and classifying spotify music listening before and during the COVID-19 pandemic in DACH countries

Cited by 8 publications

References 55 publications

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

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

Music we move to: audio features and reasons for listening

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