Large Language Models Reveal Menstruation Experiences and Needs on Social Media
Journal
MedInfo
ISBN
978-1-64368-608-0
Type
conference paper
Date Issued
2025
Author(s)
Abstract
The gender knowledge gap in medicine, particularly regarding menstruation and disorders such as endometriosis, 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 platforms 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, educational 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.
Book title
Proceedings of the 20th World Congress on Medical and Health Informatics
Volume
329
Start page
748
End page
752
Event Title
MEDINFO 2025, the 20th World Congress on Medical and Health Informatics
Event Location
Taipei
Event Date
09.08.25-13.08.25