Master of Arts
Popular Music and Culture
This thesis examines Spotify, the world’s most popular music streaming service, and its usage of music as a data extraction tool. I position Spotify as a surveillance capitalist firm that puts music at the centre of an enclosed environment designed to condition users’ affective responses and behaviors and reorient production of music. I analyze three features of the platform: a campaign in which Spotify invites users and producers to share the data it collects about them, the arrangement of the platform’s architecture into mood-based playlists, and its penchant for music that is “Chill.” I show how each serves the surveillance machine’s goals of collecting and contextualizing data from music and music consumption that it claims can quantify, predict, and condition behaviour.
Using a framework of social and economic theory alongside data and musical analysis, I position Spotify and its exploitation of music within broader implications of life under surveillance capitalism.
Summary for Lay Audience
Spotify is the world’s most popular music streaming platform, providing more than 270 million users with access to over 50 million songs, on demand. While Spotify positions itself as a neutral distributor of music, its actions are anything but neutral. In this thesis I look at the ways in which Spotify uses music to extract data from its users with the goal of predicting and eventually influencing their behaviour while I examine the ways in which it uses data to reorient the production of popular music.
Because of music’s deep connection to our sense of self and to our emotions, it acts as an effective tool for gathering data. As music consumption becomes omnipresent and ubiquitous due to portable devices and network connectivity, Spotify offers “music for every mood” and seeks to embed itself within its users’ lives to extract data from every moment.
Meanwhile, despite claims of democratizing and saving the music industry, Spotify’s market share and power subjects producers of music to a system of distribution where they must adapt to Spotify’s logic in order to find an audience. I show how Spotify diverts listeners to its branded playlists, where it controls what songs are included, and leverages its power within the industry in order to compel music makers to produce songs that operate efficiently within its organizational system.
I look at three characteristics of Spotify: “Wrapped,” a promotion in which it invites users and music producers to share their yearly Spotify statistics, creating a self-referential, closed system around them; mood-based sorting, in which Spotify creates functional and situational categories designed to provide emotion-based context for its data; and “Chill” playlists, a mood within the closed system that offers a representation of escape from making choices or making meaning.
I use a broad range of social and economic theory in collaboration with music and data analysis to illustrate how Spotify’s usage has shifted the consumption and production of popular music. I investigate and question what these changes in music can tell us about a life where we increasingly see ourselves, experience emotions, and even seek escape on the terms of those who are monitoring us.
Braun, T. Andrew, ""Dance like nobody's paying": Spotify and Surveillance as the Soundtrack of Our Lives" (2020). Electronic Thesis and Dissertation Repository. 7001.