@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://preview.aclanthology.org/fix-sig-urls/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."
}