Algorithmic Mediation and Multimodal Narratives in Tier List Videos on YouTube

This paper examines tier list videos on YouTube as a case of algorithmically mediated, multimodal interaction within online gaming communities. Building on virtual ethnography and qualitative analysis of 55 videos across globally popular games (Fortnite, Call of Duty, Counter-Strike 2, Minecraft, League of Legends), the study investigates how players construct narrative hierarchies by ranking in-game elements while combining speech, visual interfaces, and audiovisual editing. From a technical perspective, tier lists function as adaptive media artifacts: they integrate heterogeneous data (game mechanics, player experience, community feedback) and optimize their visibility through YouTube’s recommendation algorithms and platform affordances. The findings reveal that tier list videos operate as multimodal systems, where human-computer interaction, algorithmic adaptation, and cultural performance converge. By analyzing how these videos balance ludic evaluation, affective expression, and algorithmic optimization, the paper positions tier lists as exemplary cases of adaptive multimedia practices in entertainment applications, offering insights into the interplay between digital infrastructures, user performance, and participatory cultures.