Algorithmic Visibility and Automated Continuation: Infrastructural Mediation in Streaming Platforms

Abstract

AI-mediated communication is commonly associated with conversational agents, generative systems, and emotionally responsive interfaces. This article examines Top-10 rankings and autoplay as forms of infrastructural mediation embedded in streaming-platform interfaces and discusses their relevance for AI-mediated communication research. We conceptualize Top-10 rankings and autoplay as communicative arrangements embedded in the platform interface that organize visibility and interaction while shaping affective evaluation, perceived agency, and public meaning. Drawing on a cross-sectional survey of film and series club members in Spain (N = 356), the study examines two pathways. One traces the association between Top-10 consultation, perceived ranking influence, and intention–selection divergence, understood as choice drift. The other concerns the relationship between autoplay status, perceived overconsumption, and viewing-session intensity. Frequent Top-10 consultation was associated with stronger perceived influence and, through it, greater choice drift. Enabled autoplay was associated with perceived overconsumption, which in turn corresponded to more intensive viewing sessions. Session intensity showed a modest positive association with overall satisfaction. These findings suggest that streaming interfaces may participate in the mediation of communication through recommendation, visibility management, and automated continuation. By locating these processes within the infrastructure of streaming platforms, the article extends AI-mediated communication research to systems that shape interaction, evaluation, and public meaning.