A Study on the Mechanisms Underlying the Formation of Fan Data Labour Driven by Platform Algorithms: A Case Study of Fan Culture on Weibo
Abstract
The rise of social media platforms is shifting fan behavior within a celebrity worship context to be data driven and collective. This paper, based on the case of fan practices on Weibo, uses platform theory and digital labor theory to explore the formation mechanisms and main forms of platform algorithm-driven fan data labor.The study finds that mechanisms such as trending topics, recommendations, charts and rankings transform fans' emotional support into data-driven behaviors that can be recorded, calculated and compared. Fans' emotional identification motivates interactions such as 'liking', commenting, reposting, viewing and voting, leading to the emergence of collective data labor through task allocation, group supervision and data competition. These participatory activities not only boost content traffic but also enable the platform to harvest user data and create commercial value.At the same time, the boundary between emotional labor and digital labor continues to become more and more blurry, while data competition and group norms may add to the pressure of fans to maintain continuous participation. Based on this, the paper explores the connection between the emotional labor of fans, data labor and platform value. It offers a theoretical framework to understand the operational logic of fan culture in the era of digital platforms.
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