Studying the Representation of the LGBTQ+ Community in RuPaul’s Drag Race with LLM-Based Topic Modeling

Mika Hämäläinen

doi:10.18653/v1/2025.queerinai-main.1

Studying the Representation of the LGBTQ+ Community in RuPaul’s Drag Race with LLM-Based Topic Modeling

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Abstract

This study investigates the representation of LGBTQ+ community in the widely acclaimed reality television series RuPaul’s Drag Race through a novel application of large language model (LLM)-based topic modeling. By analyzing subtitles from seasons 1 to 16, the research identifies a spectrum of topics ranging from empowering themes, such as self-expression through drag, community support and positive body image, to challenges faced by the LGBTQ+ community, including homophobia, HIV and mental health. Employing an LLM allowed for nuanced exploration of these themes, overcoming the limitations of traditional word-based topic modeling.

Anthology ID:: 2025.queerinai-main.1
Volume:: Proceedings of the Queer in AI Workshop
Month:: May
Year:: 2025
Address:: Hybrid format (in-person and virtual)
Editors:: A Pranav, Alissa Valentine, Shaily Bhatt, Yanan Long, Arjun Subramonian, Amanda Bertsch, Anne Lauscher, Ankush Gupta
Venues:: QueerInAI | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 1–5
Language:
URL:: https://aclanthology.org/2025.queerinai-main.1/
DOI:: 10.18653/v1/2025.queerinai-main.1
Bibkey:
Cite (ACL):: Mika Hämäläinen. 2025. Studying the Representation of the LGBTQ+ Community in RuPaul’s Drag Race with LLM-Based Topic Modeling. In Proceedings of the Queer in AI Workshop, pages 1–5, Hybrid format (in-person and virtual). Association for Computational Linguistics.
Cite (Informal):: Studying the Representation of the LGBTQ+ Community in RuPaul’s Drag Race with LLM-Based Topic Modeling (Hämäläinen, QueerInAI 2025)
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PDF:: https://aclanthology.org/2025.queerinai-main.1.pdf

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@inproceedings{hamalainen-2025-studying,
    title = "Studying the Representation of the {LGBTQ}+ Community in {R}u{P}aul{'}s Drag Race with {LLM}-Based Topic Modeling",
    author = {H{\"a}m{\"a}l{\"a}inen, Mika},
    editor = "Pranav, A  and
      Valentine, Alissa  and
      Bhatt, Shaily  and
      Long, Yanan  and
      Subramonian, Arjun  and
      Bertsch, Amanda  and
      Lauscher, Anne  and
      Gupta, Ankush",
    booktitle = "Proceedings of the Queer in AI Workshop",
    month = may,
    year = "2025",
    address = "Hybrid format (in-person and virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.queerinai-main.1/",
    doi = "10.18653/v1/2025.queerinai-main.1",
    pages = "1--5",
    ISBN = "979-8-89176-244-2",
    abstract = "This study investigates the representation of LGBTQ+ community in the widely acclaimed reality television series RuPaul{'}s Drag Race through a novel application of large language model (LLM)-based topic modeling. By analyzing subtitles from seasons 1 to 16, the research identifies a spectrum of topics ranging from empowering themes, such as self-expression through drag, community support and positive body image, to challenges faced by the LGBTQ+ community, including homophobia, HIV and mental health. Employing an LLM allowed for nuanced exploration of these themes, overcoming the limitations of traditional word-based topic modeling."
}

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%0 Conference Proceedings
%T Studying the Representation of the LGBTQ+ Community in RuPaul’s Drag Race with LLM-Based Topic Modeling
%A Hämäläinen, Mika
%Y Pranav, A.
%Y Valentine, Alissa
%Y Bhatt, Shaily
%Y Long, Yanan
%Y Subramonian, Arjun
%Y Bertsch, Amanda
%Y Lauscher, Anne
%Y Gupta, Ankush
%S Proceedings of the Queer in AI Workshop
%D 2025
%8 May
%I Association for Computational Linguistics
%C Hybrid format (in-person and virtual)
%@ 979-8-89176-244-2
%F hamalainen-2025-studying
%X This study investigates the representation of LGBTQ+ community in the widely acclaimed reality television series RuPaul’s Drag Race through a novel application of large language model (LLM)-based topic modeling. By analyzing subtitles from seasons 1 to 16, the research identifies a spectrum of topics ranging from empowering themes, such as self-expression through drag, community support and positive body image, to challenges faced by the LGBTQ+ community, including homophobia, HIV and mental health. Employing an LLM allowed for nuanced exploration of these themes, overcoming the limitations of traditional word-based topic modeling.
%R 10.18653/v1/2025.queerinai-main.1
%U https://aclanthology.org/2025.queerinai-main.1/
%U https://doi.org/10.18653/v1/2025.queerinai-main.1
%P 1-5

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Markdown (Informal)

[Studying the Representation of the LGBTQ+ Community in RuPaul’s Drag Race with LLM-Based Topic Modeling](https://aclanthology.org/2025.queerinai-main.1/) (Hämäläinen, QueerInAI 2025)

Studying the Representation of the LGBTQ+ Community in RuPaul’s Drag Race with LLM-Based Topic Modeling (Hämäläinen, QueerInAI 2025)

ACL

Mika Hämäläinen. 2025. Studying the Representation of the LGBTQ+ Community in RuPaul’s Drag Race with LLM-Based Topic Modeling. In Proceedings of the Queer in AI Workshop, pages 1–5, Hybrid format (in-person and virtual). Association for Computational Linguistics.