Automated Visions, Algorithmic Imageflows: The Technopolitics of Black Lives Matter Videos on YouTube
by Jason W. Buel
Abstract:
This essay considers how mechanisms of machine vision intervene as forms of “social sorting” and subject formation in the context of YouTube’s algorithmic flows of images. Too often, algorithms are treated as neutral, unbiased processes. In reality, many algorithms reinscribe and reinforce human biases. This essay focuses on the power of YouTube’s algorithms to shape viewers’ understandings of the Black Lives Matter movement, focusing specifically on what Chris Ingraham calls the micro-rhetorical tier of algorithmic processing. The essay employs critical cultural studies methods to rigorously contextualize and compare case studies of algorithmically-suggested content connected to pro-Black Lives Matter videos. In this context, I argue that these automated flows of images become less about what any specific video shows about the need for radical sociopolitical change and more about the articulation of an idealized viewing position and idealized viewing subjects.
Keywords: algorithms; YouTube; Black Lives Matter; social movements; digital media
How to cite: Buel, Jason W. “Automated Visions, Algorithmic Imageflows: The Technopolitics of Black Lives Matter Videos on YouTube.” MAST, vol. 3, no. 1, April. 2022, pp. 113-133.
Copyright is retained by the authors.
© 2022 Jason W. Buel
Issue: vol. 3 no. 1 (2022): Special Issue: Automating Visuality
Section: Article
Guest Editors: Dominique Routhier, Lila Lee-Morrison, and Kathrin Maurer
Published: 25 April, 2022
