IDEAS home Printed from https://ideas.repec.org/a/cog/meanco/v8y2020i3p191-204.html
   My bibliography  Save this article

Popular Music as Entertainment Communication: How Perceived Semantic Expression Explains Liking of Previously Unknown Music

Author

Listed:
  • Steffen Lepa

    (Audio Communication Group, TU Berlin, Germany)

  • Jochen Steffens

    (Media Department, University of Applied Sciences Düsseldorf, Germany)

  • Martin Herzog

    (Audio Communication Group, TU Berlin, Germany)

  • Hauke Egermann

    (York Music Psychology Group, University of York, UK)

Abstract

Our contribution addresses popular music as essential part of media entertainment offerings. Prior works explained liking for specific music titles in ‘push scenarios’ (radio programs, music recommendation, curated playlists) by either drawing on personal genre preferences, or on findings about ‘cognitive side effects’ leading to a preference drift towards familiar and society-wide popular tracks. However, both approaches do not satisfactorily explain why previously unknown music is liked. To address this, we hypothesise that unknown music is liked the more it is perceived as emotionally and semantically expressive, a notion based on concepts from media entertainment research and popular music studies. By a secondary analysis of existing data from an EU-funded R&D project, we demonstrate that this approach is more successful in predicting 10000 listeners’ liking ratings regarding 549 tracks from different genres than all hitherto theories combined. We further show that major expression dimensions are perceived relatively homogeneous across different sociodemographic groups and countries. Finally, we exhibit that music is such a stable, non-verbal sign-carrier that a machine learning model drawing on automatic audio signal analysis is successfully able to predict significant proportions of variance in musical meaning decoding.

Suggested Citation

  • Steffen Lepa & Jochen Steffens & Martin Herzog & Hauke Egermann, 2020. "Popular Music as Entertainment Communication: How Perceived Semantic Expression Explains Liking of Previously Unknown Music," Media and Communication, Cogitatio Press, vol. 8(3), pages 191-204.
    • Handle: RePEc:cog:meanco:v:8:y:2020:i:3:p:191-204
    as

    Download full text from publisher

    File URL: https://www.cogitatiopress.com/mediaandcommunication/article/view/3153
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shayle Searle, 1995. "An overview of variance component estimation," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 42(1), pages 215-230, December.
    2. Massimo Airoldi & Davide Beraldo & Alessandro Gandini, 2016. "Follow the algorithm : An exploratory investigation of music on YouTube," Post-Print hal-02312191, HAL.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Johannes Breuer & Tim Wulf & M. Rohangis Mohseni, 2020. "New Formats, New Methods: Computational Approaches as a Way Forward for Media Entertainment Research," Media and Communication, Cogitatio Press, vol. 8(3), pages 147-152.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dana Adriana Lupșa-Tătaru & Radu Lixăndroiu, 2022. "YouTube Channels, Subscribers, Uploads and Views: A Multidimensional Analysis of the First 1700 Channels from July 2022," Sustainability, MDPI, vol. 14(20), pages 1-13, October.
    2. Fadhuile, A., 2018. "Can we explain pesticide price trend by the regulation changes ?," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277112, International Association of Agricultural Economists.
    3. Ariadna Matamoros-Fernández & Joanne E. Gray & Louisa Bartolo & Jean Burgess & Nicolas Suzor, 2021. "What’s “Up Next”? Investigating Algorithmic Recommendations on YouTube Across Issues and Over Time," Media and Communication, Cogitatio Press, vol. 9(4), pages 234-249.
    4. Jörg Wensch & Monika Wensch-Dorendorf & Hermann Swalve, 2013. "The evaluation of variance component estimation software: generating benchmark problems by exact and approximate methods," Computational Statistics, Springer, vol. 28(4), pages 1725-1748, August.
    5. Caroline Bazzoli & Sophie Lambert-Lacroix & Marie-José Martinez, 2023. "Partial least square based approaches for high-dimensional linear mixed models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 769-786, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cog:meanco:v:8:y:2020:i:3:p:191-204. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: António Vieira (email available below). General contact details of provider: https://www.cogitatiopress.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.