Evidence map›Paper›PMID 37581905›Full record

ArticleJournal of medical Internet research2023

Users' Concerns About Endometriosis on Social Media: Sentiment Analysis and Topic Modeling Study.

Rahul Goel, Vijayachitra Modhukur, Katrin Täär, Andres Salumets, Rajesh Sharma, Maire Peters

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
5.5field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Article
  3. Information-Seeking Behaviours of CALD Women with Endometriosis in Australia: A Qualitative Study.International journal of environmental research and public health · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. How Women with Endometriosis Use Social Media for Support and Self-Management: An Analysis of Reddit Content.International journal of environmental research and public health · 2025
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Following Fibroids: An Analysis of Social Media Narratives.JSLS : Journal of the Society of Laparoendoscopic Surgeons
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 2 countries.

Rahul GoelInstitute of Computer Science, University of Tartu, Tartu, Estonia.ORCID 0000-0002-2330-7369
Vijayachitra ModhukurDepartment of Obstetrics and Gynecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia.ORCID 0000-0002-7123-9903
Katrin TäärWomen's Clinic, Tartu University Hospital, Tartu, Estonia.ORCID 0009-0001-0563-7620
Andres SalumetsDepartment of Obstetrics and Gynecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia.ORCID 0000-0002-1251-8160
Rajesh SharmaInstitute of Computer Science, University of Tartu, Tartu, Estonia.ORCID 0000-0003-3581-1332
Maire PetersDepartment of Obstetrics and Gynecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia.ORCID 0000-0003-2063-242X
Competence Centre on Health Technologies (Estonia) · EEUniversity of Tartu · EEKarolinska University Hospital · SETartu University Hospital · EE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEndometriosis is a debilitating and difficult-to-diagnose gynecological disease. Owing to limited information and awareness, women often rely on social media platforms as a support system to engage in discussions regarding their disease-related concerns.

objectiveThis study aimed to apply computational techniques to social media posts to identify discussion topics about endometriosis and to identify themes that require more attention from health care professionals and researchers. We also aimed to explore whether, amid the challenging nature of the disease, there are themes within the endometriosis community that gather posts with positive sentiments.

methodsWe retrospectively extracted posts from the subreddits r/Endo and r/endometriosis from January 2011 to April 2022. We analyzed 45,693 Reddit posts using sentiment analysis and topic modeling-based methods in machine learning.

resultsSince 2011, the number of posts and comments has increased steadily. The posts were categorized into 11 categories, and the highest number of posts were related to either asking for information (Question); sharing the experiences (Rant/Vent); or diagnosing and treating endometriosis, especially surgery (Surgery related). Sentiment analysis revealed that 92.09% (42,077/45,693) of posts were associated with negative sentiments, only 2.3% (1053/45,693) expressed positive feelings, and there were no categories with more positive than negative posts. Topic modeling revealed 27 major topics, and the most popular topics were Surgery, Questions/Advice, Diagnosis, and Pain. The Survey/Research topic, which brought together most research-related posts, was the last in terms of posts.

conclusionsOur study shows that posts on social media platforms can provide insights into the concerns of women with endometriosis symptoms. The analysis of the posts confirmed that women with endometriosis have to face negative emotions and pain daily. The large number of posts related to asking questions shows that women do not receive sufficient information from physicians and need community support to cope with the disease. Health care professionals should pay more attention to the symptoms and diagnosis of endometriosis, discuss these topics with patients to reduce their dissatisfaction with doctors, and contribute more to the overall well-being of women with endometriosis. Researchers should also become more involved in social media and share new science-based knowledge regarding endometriosis.

Indexed as

EndometriosisSocial MediaFemaleHumansPainRetrospective StudiesSentiment Analysisendometriosislatent Dirichlet allocationpainRedditsentiment analysissocial mediasurgerytopic modelinguser engagement

Identifiers

PMID37581905
PMCPMC10466158
OpenAlexW4385827948

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.