Datacasting: TikTok's Algorithmic Flow as Televisual Experience

Abstract

Recommendation algorithms have acquired a central role in the suggestion of content within both subscription video on demand (SVOD) and advertising-based video on demand (AVOD) services and media-sharing platforms. In this article, we suggest the introduction of the datacasting paradigm, which takes into account the increasing relevance algorithms have in selection processes on audiovisual platforms. We use TikTok as a case study as it is an entirely algorithmic platform, and therefore embodies the heart of our discussion, and analyse how the algorithmic flow within the platform influences user experience, the impact it has on the enjoyment of content, and whether the platform can be considered televisual. We have opted to frame TikTok within debates on flow, as we believe that is what is at the core of the platform experience. Through the analysis of in-depth interviews, we extracted two main categories of responses: TV on TikTok and TikTok as TV. The former includes all responses related to the consumption of traditional televisual material on the platform, while the latter looks at all potential connections between the platform and television viewing habits.