Appraising Unmet Needs and Misinformation Spread About Polycystic Ovary Syndrome in 85,872 YouTube Comments Over 12 Years: Big Data Infodemiology Study
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Abstract
BACKGROUND: Polycystic ovary syndrome (PCOS) is the most common endocrinopathy in women, resulting in substantial burden related to metabolic, reproductive, and psychological complications. While attempts have been made to understand the themes and sentiments of the public regarding PCOS at the local and regional levels, no study has explored worldwide views, mainly due to financial and logistical limitations. YouTube is one of the largest sources of health-related information, where many visitors share their views as questions or comments. These can be used as a surrogate to understand the public's perceptions. OBJECTIVE: We analyzed the comments of all videos related to PCOS published on YouTube from May 2011 to April 2023 and identified trends over time in the comments, their context, associated themes, gender-based differences, and underlying sentiments. METHODS: After extracting all the comments using the YouTube application programming interface, we contextually studied the keywords and analyzed gender differences using the Benjamini-Hochberg procedure. We applied a multidimensional approach to analyzing the content via association mining using Mozdeh. We performed network analysis to study associated themes using the Fruchterman-Reingold algorithm and then manually screened the comments for content analysis. The sentiments associated with YouTube comments were analyzed using SentiStrength. RESULTS: A total of 85,872 comments from 940 PCOS videos on YouTube were extracted. We identified a specific gender for 13,106 comments. Of these, 1506 were matched to male users (11.5%), and 11,601 comments to female users (88.5%). Keywords including diagnosing PCOS, symptoms of PCOS, pills for PCOS (medication), and pregnancy were significantly associated with female users. Keywords such as herbal treatment, natural treatment, curing PCOS, and online searches were significantly associated with male users. The key themes associated with female users were symptoms of PCOS, positive personal experiences (themes such as helpful and love), negative personal experiences (fatigue and pain), motherhood (infertility and trying to conceive), self-diagnosis, and use of professional terminology detailing their journey. The key themes associated with male users were misinformation regarding the "cure" for PCOS, using natural and herbal remedies to cure PCOS, fake testimonies from spammers selling their courses and consultations, finding treatment for PCOS, and sharing perspectives of female family members. The overall average positive sentiment was 1.6651 (95% CI 1.6593-1.6709), and the average negative sentiment was 1.4742 (95% CI 1.4683-1.4802) with a net positive difference of 0.1909. CONCLUSIONS: There may be a disparity in views on PCOS between women and men, with the latter associated with non-evidence-based approaches and misinformation. The improving sentiment noticed with YouTube comments may reflect better health care services. Prioritizing and promoting evidence-based care and disseminating pragmatic online coverage is warranted to improve public sentiment and limit misinformation spread.
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A significant portion of PCOS-related content on social media is created by those who are selling supplements, health coaching sessions, courses, and/or consultations.
“Caution is warranted, since a significant portion of PCOS-related content on social media is created by those who are selling supplements, health coaching sessions, courses, and/or consultations [72,73].”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [72]
The Results section (page not provided) identifies spam commenters who posed as patients to sell courses and consultations. But the study analyzed YouTube comments, not the share of social-media content created by sellers; it does not establish a significant portion or document sales of supplements or health-coaching sessions. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThree of the included studies, originating from Turkey, Jamaica, and the UK, analyzed YouTube content.
“Three of the included studies, originating from Turkey [68], Jamaica [69], and the UK [72], analyzed YouTube content.”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [72]
The Methods > Data Extraction section says the study collected and analyzed comments on YouTube videos about PCOS, supporting the part of the sentence attached to this citation. The available passages do not establish that this study originated from the UK; they also cannot verify the separately cited studies from Turkey and Jamaica. (LLM, conf=0.81)
Experimental — accuracy not yet measuredTen studies included information regarding the use of social media, and two other studies that mention social media were described in the overall context of mobile apps and machine learning.
“Ten studies included information regarding the use of social media [40,41,68,69,70,71,72,73,74,75]. Two other studies that mention social media were described in the overall context of mobile apps [60] and machine learning [76] (Supplementary File S3).”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [72]
The most frequently used keyword was period (n = 9352), and the main associated theme was PCOS symptoms, such as irregular periods and acne.
“The most frequently used keyword was period (n = 9352), and the main associated theme was PCOS symptoms, such as irregular periods and acne [72].”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [72]
Most of the included social media studies (n = 6) were published in 2023, and two were published in 2022.
“Most of the included studies (n = 6) were published in 2023 [68,69,71,72,73,74], and two were published in 2022 [70,75].”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [72]
Misinformation regarding the 'cure' for PCOS was the key theme associated with comments by male users (n = 1506, 11.5%).
“Misinformation regarding the “cure” for PCOS was the key theme associated with comments by male users (n = 1506, 11.5%) [72].”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [72]
The final study, which looked at the context of comments (n = 85,872) posted on YouTube videos about PCOS (n = 940) across 12 years, found that, where it was possible to identify gender (n = 13,106), 88.5% of those comments (n = 11,601) were posted by female users.
“The final study, which looked at the context of comments (n = 85,872) posted on YouTube videos about PCOS (n = 940) across 12 years, found that, where it was possible to identify gender (n = 13,106), 88.5% of those comments (n = 11,601) were posted by female users [72].”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [72]
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References 44 · Cited by 26
- Authors
- Malhotra, K., & Kempegowda, P.
- Affiliations
- Dayanand Medical College & Hospital (IN)
- University Hospitals Birmingham NHS Foundation Trust (GB)
- Corresponding author
- Kempegowda, Punith
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2023-09-11
- Volume
- 25
- Pages
- e49220
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC10520765
- OpenAlex
- W4386593061
- Cited by
- 26
- References
- 44
- Citations by year
- 2026: 10 · 2025: 7 · 2024: 7 · 2023: 2
- FWCI
- 7.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Health › Social Media in Health Education
- Health Sciences › Medicine › Reproductive Medicine › Ovarian function and disorders
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- polycystic ovary syndrome, YouTube comments, misinformation, infodemiology, public perception, gender differences, sentiment analysis, herbal medicine, self-diagnosis, online health information, PCOS symptoms, infertility, motherhood, big data, association rule mining, network analysis, spammer reporting
- MeSH terms
- Big Data, Infodemiology, Algorithms, Female, Humans, Male, Polycystic Ovary Syndrome, Pregnancy, Social Media
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