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:
MLA (9th edition):
Buel, Jason W. “Automated Visions, Algorithmic Imageflows: The Technopolitics of Black Lives Matter Videos on YouTube.” MAST, vol. 3, no. 1, Apr. 2022, pp. 113–133.
APA (7th edition):
Buel, J. W. (2022). Automated visions, algorithmic imageflows: The technopolitics of Black Lives Matter videos on YouTube. MAST, 3(1), 113–133.
Chicago (17th edition):
Buel, Jason W. “Automated Visions, Algorithmic Imageflows: The Technopolitics of Black Lives Matter Videos on YouTube.” MAST 3, no. 1 (April 2022): 113–133.
Licensing:
This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Copyright:
Authors retain the copyright and full publishing rights without restrictions and may reuse/republish their article as part of a book or other materials, providing acknowledgment is given to MAST as the original source and place of publication. Authors can also post a copy of their accepted/published article on their websites and on their Institutional repository, citing that the article was originally published in MAST.
© 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
Submitted: Feb 15th, 2021
Accepted: Feb 1st, 2022
Published: Apr 25, 2022
