Folk theories of algorithmic recommendations on Spotify
Enacting data assemblages in the global South
Bibliographic Data
| ID | 5260621 |
|---|---|
| Authors | Ignacio Siles (0000-0002-9725-8694, Universidad de Costa Rica, corresponding author), Andrés Segura-Castillo (0000-0001-5647-1176, Distance State University), Ricardo Solís (Universidad de Costa Rica), Mónica Sancho (0000-0003-3707-062X, Universidad de Costa Rica) |
| Year | 2020 |
| Volume | 7 |
| Issue | 1 |
| Pages | 205395172092337 |
| Publication date | 2020-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Big Data & Society (JOURNAL) |
| Journal identifiers | ISSN: 2053-9517 • E-ISSN: 2053-9517 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/2053951720923377 |
| OpenAlex | W3023481589 |
| Language | EN |
| Citations received | 122 |
| References cited | 30 |
This paper examines folk theories of algorithmic recommendations on Spotify in order to make visible the cultural specificities of data assemblages in the global South. The study was conducted in Costa Rica and draws on triangulated data from 30 interviews, 4 focus groups with 22 users, and the study of "rich pictures" made by individuals to graphically represent their understanding of algorithmic recommendations. We found two main folk theories: one that personifies Spotify (and conceives of it as a social being that provides recommendations thanks to surveillance) and another one that envisions it as a system full of resources (and a computational machine that offers an individualized musical experience through the appropriate kind of "training"). Whereas the first theory emphasizes local conceptions of social relations to make sense of algorithms, the second one stresses the role of algorithms in providing a global experience of music and technology. We analyze why people espouse either one of these theories (or both) and how these theories provide users with resources to enact different modalities of power and resistance in relation to recommendation algorithms. We argue that folk theories thus offer a productive way to broaden understanding of what agency means in relation to algorithms
Big data · Data mining · Data science · Economics · Epistemology · Modalities · Social science · Sociology · Computer Science · Digital Games and Media · Ethics and Social Impacts of AI · Innovative Human-Technology Interaction
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Contexts and dimensions of algorithm literacies
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Digital intermediation
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Music streaming, cultural consumption and the everyday routines of algorithm management
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Resist or Comply
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Vernacular infrastructuring
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Genres as Social Affect
The Pitfalls and Promise of Focus Groups as a Data Collection Method
Concepts and Folk Theories
| Unique citing works | 122 |
|---|---|
| Citations per year | 20,33 |
| Citation span | 2020 - 2026 (7) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 113 |