Mediating the Marginal: A Computational Analysis of Representational Hierarchies, Aesthetic Tourism, and Queer Imagination on Instagram

Images are world-building technologies, engendering futurity through collective imagination. An ontological trace of visual culture positions media technologies as sites of both regulation of and resistance to racial, sexual, and gender norms.. The rise of computational media and neoliberal sociopolitics has paradoxically both destabilized and bolstered visual hegemony, expanding Black and queer representation and visibility through a new vanguard of empowered visual creators, while also facilitating old traditions of oppression and co-option with an unprecedented precision, surveillance, and opacity. This project leverages a computational analysis of algorithmically curated imagery to situate Instagram within a lineage of technologies used to visually mediate marginality, particularly focusing on how how race, gender, and sexuality are structured within hypersegregated queer spaces on Instagram. Analysis of skin tone presentations, emoji usage, and engagement metrics within the #gay search feed reveal a continued erasure of Blackness within mediated content, in tandem with widespread co-option of Black aesthetics. A coupled differential reading of dominant representational paradigms, hashtag usage, and normative generative modeling within the Explore feed of a gay-coded user further exposes the co-option of Black and queer aesthetics, as well as an overwhelming promotion of hypermasculine and homonormative content. These results suggest that, while contemporary visual power has certainly diffused to previously marginalized positionalities, this reallocation is contingent on market capital, assimilation to normative ideals, and continued marginality. Results are directed towards a discussion of how imagery, image-making, digital media technologies, and computation might be used in service of liberatory praxis.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Souza, Garrett

Advisors dc:contributor.advisor

• Wood, Danielle
• Turner, Katlyn

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139343

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139343