Threads of tomorrow: Reddit’s conversation on generative AI across the fashion domain

Generative AI is spreading across the fashion industry, yet public discourse about these tools remains understudied. We analyze 15,854 Reddit posts (January 2022–June 2025) using BERTopic modelling and transformer-based sentiment analysis. Through human topic screening, we identify nine fashion-related themes containing 4836 documents of theme-level analysis. Apparel design generates the most discussion and one of the highest shares of negative sentiment, revealing a ‘democratization paradox’: AI expands access to design but still requires professional refinement. Sentiment also differs by application type, with instrumental uses, such as virtual try-on and personal styling, attracting significantly less negativity than representational uses, including avatars and AI personas. Ethics discussions are dominated by AI undressing tools, a concentrated harm pattern that emerged nearly two years before the TAKE IT DOWN Act. These findings extend technology acceptance frameworks and demonstrate how social media discourse can provide early warnings of emerging risks while guiding fashion practitioners and policymakers.

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Publication Details

Journal
International Journal of Fashion Design Technology and Education
Published
2026-10-14
DOI
https://doi.org/10.1080/17543266.2026.2744645
Primary Topic
Cultural and Historical Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

Threads of tomorrow: Reddit’s conversation on generative AI across the fashion domain

Xiao Tong, Yuan Li, Arya Arun
International Journal of Fashion Design Technology and Education
Cultural and Historical Studies
article

Threads of tomorrow: Reddit’s conversation on generative AI across the fashion domain

Xiao Tong, Yuan Li, Arya Arun
article en

Abstract

Generative AI is spreading across the fashion industry, yet public discourse about these tools remains understudied. We analyze 15,854 Reddit posts (January 2022–June 2025) using BERTopic modelling and transformer-based sentiment analysis. Through human topic screening, we identify nine fashion-related themes containing 4836 documents of theme-level analysis. Apparel design generates the most discussion and one of the highest shares of negative sentiment, revealing a ‘democratization paradox’: AI expands access to design but still requires professional refinement. Sentiment also differs by application type, with instrumental uses, such as virtual try-on and personal styling, attracting significantly less negativity than representational uses, including avatars and AI personas. Ethics discussions are dominated by AI undressing tools, a concentrated harm pattern that emerged nearly two years before the TAKE IT DOWN Act. These findings extend technology acceptance frameworks and demonstrate how social media discourse can provide early warnings of emerging risks while guiding fashion practitioners and policymakers.

International Journal of Fashion Design Technology and Education
University of Alabama (US)
Openalex Percentile: Top 2%
Cultural and Historical Studies
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