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Created Plots (raw) 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.
Images 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-Hörverhalten

Persistent Identifier

Date of first publication

2021-07-30

Publisher

PsychArchives

Is referenced by

Citation

  • Author(s) / Creator(s)
    Kalustian, Kework
  • Other kind(s) of contributor
    MPI for Empirical Aesthetics, Frankfurt/Main
    en
  • PsychArchives acquisition timestamp
    2021-07-30T06:30:20Z
  • Made available on
    2021-07-30T06:30:20Z
  • Date of first publication
    2021-07-30
  • 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.
    en
  • Abstract / Description
    Images 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
  • Review status
    unknown
    en
  • Persistent Identifier
    https://hdl.handle.net/20.500.12034/4447
  • Persistent Identifier
    https://doi.org/10.23668/psycharchives.5019
  • Language of content
    eng
  • Publisher
    PsychArchives
    en
  • Is part of
    https://doi.org/10.5964/jbdgm.v30
  • Is referenced by
    https://doi.org/10.5964/jbdgm.95
  • Is related to
    https://www.psycharchives.org/handle/20.500.12034/4446
  • Is related to
    https://www.psycharchives.org/handle/20.500.12034/4448
  • Is related to
    https://doi.org/10.5964/jbdgm.95
  • Keyword(s)
    API
  • Keyword(s)
    COVID-19
  • Keyword(s)
    interpretierbares maschinelles Lernen
    de_DE
  • Keyword(s)
    k-Means Clustering
    en
  • Keyword(s)
    populäre Musik
    de_DE
  • Keyword(s)
    SVM-Klassifikator
    de_DE
  • Keyword(s)
    Streaming-Hörverhalten
    de_DE
  • Dewey Decimal Classification number(s)
    150
  • Title
    Created Plots (raw) for: Kalustian & Ruth (2021). Spotify Streaming and the COVID-19 Pandemic.
    en
  • DRO type
    image
    en