The Rise of Facial Palsy on Social Media Over the Last 5 Years
Bibliographic record
Abstract
BACKGROUND: Social media (SoMe) has become a powerful platform for distributing health information. Facial palsy (FP) results in functional and social impairment and lowers quality of life. Social media may help to raise awareness of FP sequalae. This study aims to determine the FP information growth on SoMe platforms and parameters that influence user engagement on FP content. METHODS: Five commonly used SoMe platforms (Facebook, Instagram, TikTok, Twitter, and Reddit) were analyzed. Data on 18 FP hashtags and their social interaction parameters (posts, likes, reaches, comments, shares, language, and country of origin) over the past 5 years (July 31, 2016, to July 31, 2021) were collected. In-depth account analysis was performed on the 5 most popular Instagram profiles associated with FP. RESULTS: The annual growth curve was positive on each platform. Facial Palsy Awareness Week 2021 trended best on TikTok. Facebook accumulated 315,411 likes and 1,922,678 reaches on 8356 posts. On Instagram, 24,968 posts gathered 4,904,124 likes and 9,215,852 reaches. TikTok users interacted on 3565 posts, accumulating 4,304,155 likes and 4,200,368 reaches. The implementation of reels ( P <0.001) and the profile host interacting with their followers by liking ( P <0.001) and replying ( P <0.001) to users' comments significantly increased the engagement rate. CONCLUSIONS: Facial palsy is of increasing interest on SoMe. Facial palsy surgeons may post reels, interact with their community, and engage into FPAW to promote user engagement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".