Andrés Segura-Castillo
Biographic Data
| ID | 277123 |
|---|---|
| NAME | Andrés Segura-Castillo |
| GIVEN NAMES | Andrés |
| FAMILY NAME | Segura-Castillo |
| SIGNATURE | SEGURA-CASTILLO A |
| AFFILIATIONS | Distance State University |
| ORCID | 0000-0001-5647-1176 |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 50 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 2 |
Folk theories of algorithmic recommendations on Spotify: Enacting data assemblages in the global South
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:…
Genres as Social Affect: Cultivating Moods and Emotions through Playlists on Spotify
This article bridges work on media technologies and affect theories through an analysis of how users appropriate playlists on Spotify. Our study draws on 30 interviews with users of music streaming services in Costa Rica and an analysis of their accounts on these platforms. We discuss how users create playlists as a means to cultivate affect. The notion of cultivation stresses the dynamic and ritual work involved in producing, capturing, and expl…
Folk theories of algorithmic recommendations on Spotify: Enacting data assemblages in the global South
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:…
Genres as Social Affect: Cultivating Moods and Emotions through Playlists on Spotify
This article bridges work on media technologies and affect theories through an analysis of how users appropriate playlists on Spotify. Our study draws on 30 interviews with users of music streaming services in Costa Rica and an analysis of their accounts on these platforms. We discuss how users create playlists as a means to cultivate affect. The notion of cultivation stresses the dynamic and ritual work involved in producing, capturing, and expl…
Genres as Social Affect: Cultivating Moods and Emotions through Playlists on Spotify
This article bridges work on media technologies and affect theories through an analysis of how users appropriate playlists on Spotify. Our study draws on 30 interviews with users of music streaming services in Costa Rica and an analysis of their accounts on these platforms. We discuss how users create playlists as a means to cultivate affect. The notion of cultivation stresses the dynamic and ritual work involved in producing, capturing, and expl…
Folk theories of algorithmic recommendations on Spotify: Enacting data assemblages in the global South
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:…
Computer Science (2 works) · Digital Games and Media (2 works) · Sociology (2 works) · Big data (1 works) · Communication (1 works) · Data mining (1 works) · Data science (1 works) · Economics (1 works) · Engineering (1 works) · Epistemology (1 works)