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Regimes of recognition on algorithmic media

Jacobsen, Benjamin N

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Abstract

This article examines ways in which people are seen, recognised, and made to matter by social media platforms. Drawing on Louise Amoore’s notion of ‘regimes of recognition’, I argue that social media platforms can be conceptualised as increasingly powerful arbiters of recognisability, determining the conditions of possibility of how people are seen and come to matter. Through an analysis of Twitter’s saliency detection algorithm, which automatically crops images uploaded to the platform, the article highlights how social media platforms participate in producing novel modes of recognisability, that is, conditions by which people are rendered visible and invisible within or by the platform. Moreover, the article highlights how regimes of recognition on algorithmic media shape people’s parameters of attention and perception more generally through what I call the automatic production of ‘consistent’ lines of sight. Ultimately, the article seeks to highlight how the notion of recognition is increasingly arbitrated in and through algorithmic media and how this is fraught with political issues and tension. As such, there is an ongoing need to critically examine the power of social media to render people visible and invisible.

Citation

Jacobsen, B. N. (2023). Regimes of recognition on algorithmic media. New Media and Society, 25(12), 3641–3656. https://doi.org/10.1177/14614448211053555

Journal Article Type Article
Acceptance Date Sep 28, 2021
Online Publication Date Oct 26, 2021
Publication Date 2023-12
Deposit Date Jan 19, 2022
Publicly Available Date Jan 20, 2022
Journal New Media & Society
Print ISSN 1461-4448
Electronic ISSN 1461-7315
Publisher SAGE Publications
Peer Reviewed Peer Reviewed
Volume 25
Issue 12
Pages 3641–3656
DOI https://doi.org/10.1177/14614448211053555
Public URL https://durham-repository.worktribe.com/output/1216444

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
Advance online version This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages





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