Remixing “Taste”

Authorship and Attribution in Spotify Blend Playlists

Authors

  • Morghen Jael Faculty of Information, University of Toronto

DOI:

https://doi.org/10.29173/pathfinder81

Keywords:

Spotify Blend, digital music playlists, remix, mashup, authorship, attribution, collaboration

Abstract

In this paper, I discuss dimensions of remix, including attribution and authorship, for automated digital music playlists. I use the case study of Spotify Blend, an automated, personalized, mock-collaborative playlist feature that combines up to ten users’ music taste and listening histories and regenerates its content daily. I defend the characterization of Spotify Blend as an example of “remix” (or an example of “mashup,” a related concept), wherein the source material being remixed is user listening data and wherein sampling is the primary remix tactic. In fleshing out this characterization, I discuss how the concepts of “authorship” and “attribution” operate in the context of Spotify Blend, with the important acknowledgment that Spotify’s algorithm remains opaque to users. I also compare Spotify Blend with user-generated, actively collaborative playlists created on the same platform. Observations about Blend and its features of use derive mostly from personal experience with the program.

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Published

2023-09-08

How to Cite

Jael, M. (2023). Remixing “Taste”: Authorship and Attribution in Spotify Blend Playlists. Pathfinder: A Canadian Journal for Information Science Students and Early Career Professionals, 4(1), 28–43. https://doi.org/10.29173/pathfinder81

Issue

Vol. 4 No. 1 (2023): Regular Issue

Section

Scholarly Articles

License

Copyright (c) 2023 Morghen Jael

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