Folk theories of algorithmic recommendations on Spotify: Enacting data assemblages in the global South
Folk theories of algorithmic recommendations on Spotify: Enacting data assemblages in the global South
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Spotify 算法推荐的民间理论：在南半球实施数据组合
DOI：
10.1177/2053951720923377
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发表时间：
2020
影响因子：
8.5
通讯作者：
Mónica Sancho
中科院分区：
文献类型：
--
作者：
I. Siles;Andrés Segura;R. Solís;Mónica Sancho
本文研究了 Spotify 算法推荐的民间理论，以便让南方国家数据组合的文化特性变得可见。该研究在哥斯达黎加进行，利用了 30 次访谈、4 个焦点小组（22 位用户）的三角数据，以及对个人制作的“丰富图片”的研究，以图形方式表示他们对算法建议的理解。我们发现了两种主要的民间理论：一种将 Spotify 拟人化（并将其视为一种通过监视提供建议的社会存在），另一种将其设想为一个充满资源的系统（以及一台通过适当的“训练”提供个性化音乐体验的计算机）。第一个理论强调社会关系的局部概念来理解算法，而第二个理论则强调算法在提供全球音乐和技术体验方面的作用。我们分析了为什么人们拥护这些理论之一（或两者），以及这些理论如何为用户提供资源来制定与推荐算法相关的不同形式的权力和阻力。因此，我们认为民间理论提供了一种有效的方式来扩大对代理与算法相关含义的理解。
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.