Dataset for: Kalustian & Ruth (2021). Spotify Streaming and the COVID-19 Pandemic.
Author(s) / Creator(s)
Kalustian, Kework
Other kind(s) of contributor
MPI for Empirical Aesthetics, Frankfurt/Main
Abstract / Description
Many people used musical media via music streaming service providers to cope with the limitations of the COVID-19 pandemic. Accounting for such behavior from the perspective of uses-and-gratifications theory and situated cognition yields reliable explanations regarding people’s active and goal-oriented use of musical media. We accessed Spotify’s daily top 200 charts and their audio features from the DACH countries for the period during the first lockdown in 2020 and a comparable non-pandemic period situation in 2019 to support those theoretical explanations quantitatively with open data. After exploratory data analyses, applying a k-means clustering algorithm across the DACH countries allowed us to reduce the dimensionality of selected audio features. Following these clustering results, we discuss how these clusters are explainable using the arousal-valence-circumplex model and possibly be understood as (gratification) potentials that listeners can interact with to modulate their moods and thus emotionally cope with the stress of the pandemic. Then, we modeled a cross-validated binary SVM classifier to classify the two periods based on the extracted clusters and the remaining manifest variables (e.g., chart position) as input variables. The final test scenario of the classification task yielded high overall accuracy in classifying the periods as distinguishable classes. We conclude that these demonstrated approaches are generally suitable to classify the two periods based on the extracted mood clusters and the other input variables, and furthermore to interpret, by considering the model-related caveats, everyday music listening via those proxy variables as an emotion-focused coping strategy during the COVID-19 pandemic in DACH countries.
Dataset for: Kalustian, K., & Ruth, N. (2021). “Evacuate the Dancefloor”: Exploring and Classifying Spotify Music Listening Before and During the COVID-19 Pandemic in DACH Countries. In: T. Fischinger, & C. Louven, C. (Eds.), Musikpsychologie – Empirische Forschungen - Ästhetische Experimente, Band 30.
Keyword(s)
API COVID-19 interpretierbares maschinelles Lernen k-Means Clustering populäre Musik SVM-Klassifikator Streaming-HörverhaltenPersistent Identifier
Date of first publication
2021-07-30
Publisher
PsychArchives
Is referenced by
Citation
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AT_Tracks_19.csvCSV - 1.51MBMD5: 98a0166c206dcaff2e4a6acc3babeb89Description: Retrieved & Aggregated Dataset for: Kalustian & Ruth (2021)
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AT_Tracks_20.csvCSV - 1.49MBMD5: e347aa9049c1d6ee0ed452927e0b1bb4Description: Retrieved & Aggregated Dataset for: Kalustian & Ruth (2021)
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CH_Tracks_19.csvCSV - 1.53MBMD5: cacd5ed6337865aba9f545744847a261Description: Retrieved & Aggregated Dataset for: Kalustian & Ruth (2021)
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CH_Tracks_20.csvCSV - 1.51MBMD5: 6a1413b3c03361d23e613024127958c7Description: Retrieved & Aggregated Dataset for: Kalustian & Ruth (2021)
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DE_Tracks_19.csvCSV - 1.53MBMD5: 746975751b2696db5be1d9e09dfb9461Description: Retrieved & Aggregated Dataset for: Kalustian & Ruth (2021)
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DE_Tracks_20.csvCSV - 1.5MBMD5: 1cb7235470486ab5d5257ab439a71e78Description: Retrieved & Aggregated Dataset for: Kalustian & Ruth (2021)
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DACH_tracks.csvCSV - 9.06MBMD5: e97b9cacbf8838f42ca0ee69d9243bfeDescription: Retrieved & Aggregated Dataset for: Kalustian & Ruth (2021)
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DACH_complete.csvCSV - 36.69MBMD5: bedebfe918ea165e610322f664fc5e7fDescription: Retrieved & Aggregated Dataset for: Kalustian & Ruth (2021)
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df_ml_full.csvCSV - 28.09MBMD5: 2fd86106641de895e9ad30010e022468Description: Retrieved & Aggregated Dataset for: Kalustian & Ruth (2021)
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df_ml.csvCSV - 27.86MBMD5: 9e918d3642d554d3d7eba5cc799d3c1eDescription: Retrieved & Aggregated Dataset for: Kalustian & Ruth (2021)
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Codebook.htmlHTML - 15.55MBMD5: 55c9898ef68e64b9f3b894cdd242542eDescription: Codebook for: Kalustian & Ruth (2021)
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Codebook_htmlcopy.pdfAdobe PDF - 994.68KBMD5: f15e5ecf5631540fd3bd5f65a89e8db3Description: Codebook for: Kalustian & Ruth (2021)
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There are no other versions of this object.
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Author(s) / Creator(s)Kalustian, Kework
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Other kind(s) of contributorMPI for Empirical Aesthetics, Frankfurt/Main
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PsychArchives acquisition timestamp2021-07-30T06:30:22Z
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Made available on2021-07-30T06:30:22Z
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Date of first publication2021-07-30
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Abstract / DescriptionMany people used musical media via music streaming service providers to cope with the limitations of the COVID-19 pandemic. Accounting for such behavior from the perspective of uses-and-gratifications theory and situated cognition yields reliable explanations regarding people’s active and goal-oriented use of musical media. We accessed Spotify’s daily top 200 charts and their audio features from the DACH countries for the period during the first lockdown in 2020 and a comparable non-pandemic period situation in 2019 to support those theoretical explanations quantitatively with open data. After exploratory data analyses, applying a k-means clustering algorithm across the DACH countries allowed us to reduce the dimensionality of selected audio features. Following these clustering results, we discuss how these clusters are explainable using the arousal-valence-circumplex model and possibly be understood as (gratification) potentials that listeners can interact with to modulate their moods and thus emotionally cope with the stress of the pandemic. Then, we modeled a cross-validated binary SVM classifier to classify the two periods based on the extracted clusters and the remaining manifest variables (e.g., chart position) as input variables. The final test scenario of the classification task yielded high overall accuracy in classifying the periods as distinguishable classes. We conclude that these demonstrated approaches are generally suitable to classify the two periods based on the extracted mood clusters and the other input variables, and furthermore to interpret, by considering the model-related caveats, everyday music listening via those proxy variables as an emotion-focused coping strategy during the COVID-19 pandemic in DACH countries.
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Abstract / DescriptionDataset for: Kalustian, K., & Ruth, N. (2021). “Evacuate the Dancefloor”: Exploring and Classifying Spotify Music Listening Before and During the COVID-19 Pandemic in DACH Countries. In: T. Fischinger, & C. Louven, C. (Eds.), Musikpsychologie – Empirische Forschungen - Ästhetische Experimente, Band 30.en
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Review statusunknownen
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/4448
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.5020
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Language of contenteng
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PublisherPsychArchivesen
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Is part ofhttps://doi.org/10.5964/jbdgm.v30
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Is referenced byhttps://doi.org/10.5964/jbdgm.95
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Is related tohttps://hdl.handle.net/20.500.12034/5438
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Is related tohttps://www.psycharchives.org/handle/20.500.12034/4446
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Is related tohttps://www.psycharchives.org/handle/20.500.12034/4447
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Keyword(s)API
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Keyword(s)COVID-19
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Keyword(s)interpretierbares maschinelles Lernende_DE
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Keyword(s)k-Means Clusteringen
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Keyword(s)populäre Musikde_DE
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Keyword(s)SVM-Klassifikatorde_DE
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Keyword(s)Streaming-Hörverhaltende_DE
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Dewey Decimal Classification number(s)150
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TitleDataset for: Kalustian & Ruth (2021). Spotify Streaming and the COVID-19 Pandemic.en
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DRO typeresearchDataen