Mediating The Marginal: A Quantitative Analysis of Curated LGBTQ+ Content on Instagram
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3706598.3713618.pdf
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5.58 MB
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Adobe PDF
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Author(s)
Souza, Garrett
Lutz, Nina
Turner, Katlyn
Date Issued
April 25, 2025
Publisher
ACM|CHI Conference on Human Factors in Computing Systems
Citation
Garrett Souza, Nina Lutz, and Katlyn M Turner. 2025. Mediating The Marginal: A Quantitative Analysis of Curated LGBTQ+ Content on Instagram. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25). Association for Computing Machinery, New York, NY, USA, Article 791, 1–20.
Version
Final published version
Abstract
Control and curation of dominant visual culture – rendering who and what is visible – is central to identity formation, particularly for LGBTQ+ communities relying on digital spaces for safe self-expression. In this work, we analyze Instagram as a site of algorithmic visual curation, performing a quantitative analysis of algorithmically mediated image feeds delivered to a gay-coded user. Our persona account exclusively followed #gay and #instagay feeds, and engaged in content within these discursive spaces to seed algorithmic content promotion to a normative gay user. We present an analysis of skin tone presentations, emoji usage, and engagement metrics alongside analysis of generative outputs of dominant visual trends within the #gay search and Explore feeds. We observe content depicting darker-skinned individuals has higher engagement yet less algorithmic promotion relative to lighter skin tones, while hypermasculine and homonormative content is heavily promoted. These results suggest that, while marginalized positionalities have certainly been rendered more visible through social media platforms, this visibility is increasingly contingent on assimilation to normative ideals through algorithmically determined modes that are not necessarily consistent with user choices, preferences, or realities.
Description
CHI ’25, Yokohama, Japan
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3706598.3713618
