Purpose

This paper examines promotional practices Netflix employs via Twitter and its automated recommendation system in order to deepen our understanding of how streaming services contribute to sociotechnical inequities under capitalism.

Design/methodology/approach

Tweets from two Netflix Twitter accounts as well as material features of Netflix's recommendation system were qualitatively analyzed using inductive analysis and the constant comparative method in order to explore dimensions of Netflix's promotional practices.

Findings

Twitter accounts and the recommendation system profit off people's labor to promote content, and such labor allows Netflix to create and refine classification practices wherein both people and content are categorized in inequitable ways. Labor and classification feed into Netflix's production of culture via appropriation on Twitter and algorithmic decision-making within both the recommendation system and broader AI-driven production practices.

Social implications

Assemblages that include algorithmic recommendation systems are imbued with structural inequities and therefore unable to be fixed by merely diversifying cultural industries or retooling algorithms on streaming platforms. It is necessary to understand systemic injustices within these systems so that we may imagine and enact just alternatives.

Originality/value

Findings demonstrate that via surveillance tactics that exploit people's labor for promotional gains, enforce normative classification schemes, and culminate in normative cultural productions, Netflix engenders practices that regulate bodies and culture in ways that exemplify interconnections between people, machines, and social institutions. These interconnections further reflect and result in material inequities that crystalize within sociotechnical processes.

中文翻译：

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Netflix的文化工作，分类和生产

目的

本文研究了Netflix通过Twitter及其自动推荐系统采用的促销做法，以加深我们对流媒体服务如何在资本主义下加剧社会技术不平等的理解。

设计/方法/方法

使用归纳分析和恒定比较方法定性分析了来自两个Netflix Twitter帐户的推文以及Netflix推荐系统的实质功能，以探索Netflix促销活动的范围。

发现

Twitter帐户和推荐系统从人们的劳动内容推广中获利，而这种劳动使Netflix可以创建和完善分类实践，其中以不平等的方式对人和内容进行分类。劳动和分类通过推特上的拨款以及推荐系统和更广泛的AI驱动的生产实践中的算法决策，将其纳入Netflix的文化生产。

社会影响

包括算法推荐系统的程序集结构不平等，因此无法通过仅多样化文化产业或在流媒体平台上重新构建算法来解决。有必要了解这些系统中的系统性不公正，以便我们可以想象和制定替代方案。

创意/价值

研究结果表明，通过利用监视手段来剥削人们的劳动以促进收益，执行规范的分类方案并最终达到规范的文化产品，Netflix催生了规范身体和文化的实践，从而体现了人，机器和社会机构之间的相互联系。这些相互联系进一步反映并导致了在社会技术过程中结晶的物质不平等。