"Dance Like Nobody's Paying": Spotify and Surveillance As the Soundtrack of Our Lives
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Western University Scholarship@Western Electronic Thesis and Dissertation Repository 5-11-2020 1:00 PM "Dance like nobody's paying": Spotify and Surveillance as the Soundtrack of Our Lives T. Andrew Braun, The University of Western Ontario Supervisor: Stahl, Matt, The University of Western Ontario A thesis submitted in partial fulfillment of the equirr ements for the Master of Arts degree in Popular Music and Culture © T. Andrew Braun 2020 Follow this and additional works at: https://ir.lib.uwo.ca/etd Part of the Communication Technology and New Media Commons, and the Other Music Commons Recommended Citation Braun, T. Andrew, ""Dance like nobody's paying": Spotify and Surveillance as the Soundtrack of Our Lives" (2020). Electronic Thesis and Dissertation Repository. 7001. https://ir.lib.uwo.ca/etd/7001 This Dissertation/Thesis is brought to you for free and open access by Scholarship@Western. It has been accepted for inclusion in Electronic Thesis and Dissertation Repository by an authorized administrator of Scholarship@Western. For more information, please contact [email protected]. Abstract 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. Keywords Spotify, music streaming, surveillance capitalism, data gathering, individual/dividual, playlists, mood-based music, chill music. ii 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. 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. iii Acknowledgments Thank you to my supervisor, Dr. Matt Stahl for knowing when to let me wander around in this maze and knowing how and when to guide me through. Thank you to Dr. Alison Hearne for her insight as my second reader and to the other FIMS faculty who have helped along the way including Dr. Norma Coates, Dr. Keir Keightley, and Dr. Tom Streeter. Infinite thanks to my mom and dad. And to Laura for enduring this time in “the in-between.” iv Table of Contents Abstract ............................................................................................................................... ii Summary for Lay Audience ............................................................................................... iii Acknowledgments .............................................................................................................. iv Table of Contents ................................................................................................................ v 1 Introduction: “On demand” ....................................................................................... 1 1.1 Introduction and main research questions ............................................................... 1 1.2 Analytical Framework ............................................................................................ 4 1.2.1 The Spotify machine ................................................................................... 4 1.2.2 Music and the code of surveillance capitalism ........................................... 7 1.2.3 Rendering data from music ......................................................................... 9 1.3 Contextualizing Spotify ........................................................................................ 12 1.3.1 Market share and product differentiation .................................................. 12 1.3.2 Free to be a target ...................................................................................... 14 1.3.3 Savior or new “gatekeeper”? .................................................................... 16 1.3.4 The branded playlist is king ...................................................................... 20 1.4 Literature Review .................................................................................................. 23 1.4.1 Key texts ................................................................................................... 23 1.4.2 Chapter specific literature ......................................................................... 30 1.4.3 The algorithm ............................................................................................ 34 1.5 Methodology ......................................................................................................... 35 1.5.1 Data collection .......................................................................................... 35 1.5.2 Case study selection .................................................................................. 38 1.5.3 Musical analysis ........................................................................................ 39 2 Believe: “Be free” ....................................................................................................... 40 2.1 Wrapping Belief .................................................................................................... 44 2.1.1 Ragle Gumm is wrapped ........................................................................... 44 2.1.2 Producer case study #1: Zola Jesus – recontextualizing “Wrapped” ........ 47 2.2 Using Belief .......................................................................................................... 55 2.2.1 Habituation ................................................................................................ 55 2.2.2 User wrap .................................................................................................. 60 2.3 Producing Belief ................................................................................................... 63 v 2.3.1 Internalizing “Wrapped” ........................................................................... 63 2.3.2 A human touch: unpacking the promise of data ....................................... 65 2.3.3 Producer case study #2: Neanderthal – accepting the terms ..................... 68 2.3.4 Producer case study #3: Nina Nesbitt – the feeling’s mutual ................... 72 2.3.5 Out of joint: outrage .................................................................................. 77 2.4 Conclusions ........................................................................................................... 80 3 Forget: “Be happy” .................................................................................................... 82 3.1 The Essentials ....................................................................................................... 85 3.1.1 Playlist territorialization ............................................................................ 85 3.1.2 Darwinian data: building musical