2025
NAACL
NAACL 2025
Studying the Representation of the LGBTQ+ Community in RuPaul’s Drag Race with LLM-Based Topic Modeling
Abstract
AbstractThis 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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Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary and Machine Learning and Natural Language Processing
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Keyword Pioneer
— lgbtq+ representation
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Learning Types > Unsupervised Learning
Natural Language Processing > Resources & Methods > Large Language Models
Interdisciplinary > Science > Digital Humanities
Machine Learning > Learning Types > Representation Learning
Artificial Intelligence > Core AI > Large Language Models
Interdisciplinary > Digital Humanities