Large Language Models Reveal Menstruation Experiences and Needs on Social Media

Charlotte Tumescheit, Davinny Sou, Marcia Nißen, Tobias Kowatsch, Janna Hastings
Studies in health technology and informatics · 2025 · vol. 329 , pp. 748–752 · doi:10.3233/SHTI250940 · PMID:40775958
other OA: hybrid CC-BY-NC-4.0
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⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

This study utilized large language models to analyze social media comments, identifying key themes of emotional support, educational guidance, and community validation in menstruation experiences and needs.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-07 · read from full text ⓘ

The paper studies how social media (TikTok and YouTube) reflects menstruation lived experiences and information needs, motivated by a stated gender knowledge gap in medicine for menstruation-related disorders including endometriosis. Using a novel pipeline, the authors analyzed video comments with BERTopic and the Llama 3.1 large language model to extract recurring themes, finding key topics such as emotional support, educational guidance, and community validation that were consistent with prior work. A major limitation is that the analysis is based on social media content and is not framed as a clinical assessment of diagnoses or symptom severity, despite being used to inform research directions. Relevance to endometriosis: the paper explicitly mentions endometriosis in its motivation as part of the broader menstruation/disorders knowledge gap, though its main focus is computational topic extraction of menstruation experiences on social media rather than endometriosis specifically.

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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.
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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.

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endometriosis

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Language Language Language Language Language Language Language Language Language Language Language Language Language Language Language Language Language Language Language Language

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europepmc
last seen: 2026-06-24T06:10:11.469335+00:00
pubmed
last seen: 2026-06-24T06:06:43.627525+00:00
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last seen: 2026-05-11T08:34:28.763810+00:00
License: CC-BY-NC-4.0 · commercial use OK · attribution required
Courtesy of the U.S. National Library of Medicine