Gavin Feller: The cultural implications of YouTube's video recommendation system
From Tatyana Sarayeva
Gavin Feller is a Postdoctoral fellow at Humlab, Umeå University
Abstract: This presentation explores AI through research on YouTube’s video recommendation system. I draw on empirical findings from an ongoing study of video meme circulation on YouTube in order to expand the concept of AI to include the many technical systems powered by machine learning that individuals interact with on a daily basis. The study involves tracing the flow of YouTube’s recommendation system in order to understand how it treats video memes—a relatively recent type of content based on bottom-up appropriation, remix, and re-circulation. How YouTube’s recommendation system understands video memes can teach us about how machine learning systems interact with cultural content that does not easily fit into pre-existing genres and categories. As meme culture continues to expand—as evident in the overwhelming popularity of TikTok, for instance—researchers of AI should pay closer attention to the influence of the many recommendation systems embedded in the infrastructures of today’s most dominant digital communication and media technologies.
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