When streamed music is exhibited on streaming platforms, it is often presented in a friendly and collective manor. Music files are frequently made to socialize with each other by being bundled up, or tied together through trails of data that detect similar artists, common histories, or auditory commonalities between sounds and artists. These alliances, or bonds, between music files and musicians do not just shape how music is framed, but also how it is enjoyed. The social networks of streamed music files are what underlie promises to enhance musical discoveries and listening experiences.
Streamed music files are frequently also made to socialize with audiences, since their movement and display is largely connected to the analysis of actual user practices–or estimations thereof. In a recent experiment Anna Johansson and Maria Eriksson are exploring how music recommendations are entangled with fantasies of listening–often based estimations of taste depending on user’s age, gender, and geography. By capturing and analyzing the music recommendations Spotify delivers to a selected number of pre-designed Spotify users, their experiment sets out to explore how the Spotify client, and it’s algorithms, are performative of user identities and taste constellations.
Partly informed by recent works in the field of software studies, their work address questions regarding the tension between (imaginary) publics and Spotify’s promise to deliver individualized or highly personalized music recommendations to everyone. It also ties into broader questions about the workings and effects of algorithmic knowledge production. An article is currently under way, and will be published in the journal Cultural Analysis during 2016.