Watch, Scroll, Repeat: How Interface Design Shapes Consumptive Curation Affordances on TikTok

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. This article interrogates the role of TikTok’s interface design in shaping these new consumptive curation affordances. Drawing on Davis’s concept of consumptive curation – users’ selective engagement with vast pools of content – and literature on social media affordances and mediation theory, I present consumptive curation affordances as relational: shaped by the interplay between platforms’ technological design, user practices and social arrangements. TikTok’s interface is central in this interplay, mediating consumptive curation practices with algorithmic recommendations through 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 affordance mechanisms. Findings reveal the dominant role of the For You Page, where TikTok strongly encourages users toward passive consumptive curation – watch, scroll, repeat – 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 offers a theoretical foundation for understanding how interface design influences consumptive curation affordances. Grounded in a relational view of affordances, future studies can explore how socially situated users strategise interactions with TikTok’s algorithmic environment.