View Item 
        •   Utrecht University Student Theses Repository Home
        • UU Theses Repository
        • Theses
        • View Item
        •   Utrecht University Student Theses Repository Home
        • UU Theses Repository
        • Theses
        • View Item
        JavaScript is disabled for your browser. Some features of this site may not work without it.

        Browse

        All of UU Student Theses RepositoryBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

        Data and Social Surveillance in Music Streaming: Pseudo-Personalization and The Case of Spotify’s “Friend Activity” Feature

        Thumbnail
        View/Open
        Voulgarakis 1060678.docx (122.5Kb)
        Publication date
        2025
        Author
        Voulgarakis, Ilias
        Metadata
        Show full item record
        Summary
        In recent years, the personalization of content on digital platforms has become a widely debated phenomenon, especially in discussions around algorithmic surveillance, and user agency. However, despite significant attention to data surveillance in music streaming (Eriksson et al. 2019; Drott 2024; Walsh 2024), the social dimension of surveillance—particularly through features like Spotify’s Friend Activity—remains critically underexplored. This thesis addresses that gap by examining how Spotify’s social interface functions as a site of both algorithmic and social surveillance that may influence users’ listening behaviors. Through a qualitative approach, I analyze the “Friend Activity” feature on Spotify, drawing from semi-structured interviews with users, a review of scholarly literature on algorithmic power, and theories of surveillance, including Foucault’s “panopticon” (Foucault 1995), the “omniopticon,” and Zuboff’s “surveillance capitalism” (Zuboff 2019). I also consider frameworks such as Cheney-Lippold’s algorithmic identity (Cheney-Lippold 2017), and Adorno and Horkeimer’s critique of cultural standardization (Adorno and Horkheimer 2016). I argue that the “Friend Activity” feature fosters a sense of being watched that shapes music consumption, influencing not only what users listen to, or how they listen to, but also how they perceive themselves in relation to others. This surveillance operates through a dual logic: the opaque, personalized algorithm on one side and the social visibility on the other, creating a space where algorithmic and social pressures converge. My contribution lies in exploring the social mechanisms of influence embedded in digital music platforms like Spotify, and in calling for greater awareness of how platform design—beyond data-driven personalization—shapes user behavior. Ultimately, I propose that social features like “Friend Activity” play an underacknowledged role in the shaping of musical behavior and identity under platform capitalism.
        URI
        https://studenttheses.uu.nl/handle/20.500.12932/49336
        Collections
        • Theses
        Utrecht university logo