探索用户在音乐流媒体环境中的算法知识和反身性:批判现实主义方法

Sebastian Cole
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引用次数: 0

摘要

Spotify 等数字平台具有特定的特点和属性,这些特点和属性在一定程度上影响着平台的使用方式。然而,用户会对这些特性做出自己的解释,并以独特的方式与平台互动。本研究采用批判现实主义框架,以算法推荐系统为例,探讨在 Spotify 的背景下如何实践反身性模式。从这个角度来看,反身性是指一个人在决定如何行动之前,对其背景、数据、以往经验和知识等要素进行反思的能力。对 Spotify 用户的访谈结果表明,参与者在解读 Spotify 的推荐并决定听什么时,会采用多种反身性模式。这些模式取决于每位参与者的关注点和算法知识。
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Exploring users’ algorithmic knowledge and reflexivity in a music streaming context: A critical realist approach
Digital platforms such as Spotify have specific characteristics and properties that influence, to some extent, how the platform is used. However, users develop their own interpretations of these properties as well as unique ways to engage with the platform. This study applies a critical realist framework to explore how reflexivity modes are practiced in the context of Spotify as an example of algorithmic recommendation systems. From this perspective, reflexivity is a person’s capacity to reflect on their contexts, data, previous experiences, and knowledge, among other elements, before deciding how to act. Findings from interviews with Spotify users suggest that participants practice multiple reflexivity modes when interpreting Spotify’s recommendations and deciding what to listen to. These modes depend on each participant’s concerns and algorithmic knowledge.
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CiteScore
5.80
自引率
7.10%
发文量
98
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