Large Language Models Reveal Menstruation Experiences and Needs on Social Media
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Author / Producer
Date
2025
Publication Type
Conference Paper
ETH Bibliography
yes
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Data
Abstract
The gender knowledge gap in medicine, particularly regarding menstruation and disorders such as en-dometriosis, often results in delayed diagnoses and inadequate care. Many menstruating individuals report dismissal of debilitating symptoms, driving them to seek information and support on online plat-forms such as TikTok and YouTube. This study leverages social media to identify key topics reflecting lived experiences and needs to bridge this knowledge gap. Using a novel pipeline, we analysed video comments using BERTopic and the Llama 3.1 model. Key topics, including emotional support, educa-tional guidance, and community validation, were consistent with prior research. This study underscores the potential of social media and large language models to inform inclusive menstrual health research, revealing unique insights regarding the menstruation experiences and needs of underrepresented and historically overlooked individuals such as those with irregular cycles.
Permanent link
Publication status
published
External links
Book title
MEDINFO 2025 — Healthcare Smart × Medicine Deep
Journal / series
Volume
329
Pages / Article No.
748 - 752
Publisher
IOS Press
Event
20th World Congress on Medical and Health Informatics (MEDINFO 2025)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
LLM; NLP; Topic modelling; Llama; Women's health; Menstrual health; Menstruation; Individual needs and experiences; Social media
Organisational unit
03681 - Fleisch, Elgar / Fleisch, Elgar
02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.