A Comparison of Lichen Sclerosus and Vulvodynia Content Across Social Media Platforms: What Is Trending Over Time
Bibliographic record
Abstract
OBJECTIVES: Vulvovaginal diseases are common gynecologic complaints and patients often turn to social media (SM) for medical information. The objective of this study is to examine vulvovaginal content on SM and how it has changed over time. MATERIALS AND METHODS: Four SM platforms were searched (i.e., Facebook, Instagram, Twitter, and YouTube) at 2 time points from March 30 to May 7, 2021, and again from November 24 to December 10, 2022. Newer SM platforms became popular during this time interval and thus TikTok and Reddit were included in the search in 2022. This study focused on 2 common vulvovaginal conditions: lichen sclerosus and vulvodynia. The SM platforms were searched for content on these conditions and the type of content, language, and country of origin were assessed. RESULTS: A total of 1228 SM accounts, posts, and pages were assessed. Lichen sclerosus content on SM was mostly informational (32.6%), whereas vulvodynia content was mostly personal experience (30.5%). Patient support groups were significantly more popular in 2021 compared with 2022 and professional groups were more common in 2022 compared with 2021 ( p < .001). Overall, Facebook and Instagram consisted mostly of patient support groups, YouTube had both informational and professional videos, TikTok had mostly personal experiences and healthcare professional videos, and Reddit was mostly discussions about patient personal experiences. CONCLUSIONS: The current study highlights the content and quantifies user engagement of lichen sclerosus and vulvodynia on SM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".