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Description
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Social media’s transition into algorithmic content recommendations, accelerated by TikTok’s entry into the ecosystem, has reshaped platforms’ consumptive curation affordances, reducing users’ ability to curate their feeds directly. While previous research has explored user experiences with TikTok’s algorithmic recommendations, there has been limited attention to how its interface shapes these interactions. In this article, I interrogate the role of TikTok’s interface design in shaping these new consumptive curation affordances. Building on Davis’s concept of consumptive curation—users’ selective engagement with vast pools of content—and drawing from literature on social media affordances and mediation theory, I present consumptive curation affordances as relational. They are shaped by the interplay between social media technological design, user practices, and social arrangements. TikTok’s interface is central in this interplay, mediating consumptive curation practices with algorithmic recommendations and shaping such interactions via several affordance mechanisms. I analyse TikTok’s interface through a walkthrough method, organised according to the algorithmic experience framework, where I operationalise the concepts of friction levels and affordances mechanisms. Findings reveal the dominant role of the For You Page, where TikTok strongly encourages users toward passive consumptive curation—watching, scrolling, and repeating—while refusing to provide enough transparency about how interactions curate recommendations and discouraging users from disabling data collection. As a result, TikTok’s interface discourages users from strategising consumptive curation practices, demanding reliance on opaque algorithmic recommendations. This study deepens our understanding of how interface design influences consumptive curation affordances, offering a theoretical foundation for future research. Grounded in a relational view of affordances, subsequent studies can further explore how users, shaped by their social contexts, develop strategies to interact with TikTok’s algorithmic environment. (2025-05-21)
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Notes
| The walkthrough was performed for two months (16-10-23 to 16-12-23) from Seville, Spain. The device used was a Redmi Note Pro 9, OS version MIUI 14 (14.0.3.0.SJZMIXM). TikTok app’s version was v31.7.4(2023107040). |
