"Dance like nobody's paying": Spotify and Surveillance as the Soundtrack of Our Lives
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.