Negative sentiments toward anterior cruciate ligament injury prevention outweigh positive awareness discussions on social media
Bibliographic record
Abstract
OBJECTIVES: The aim of this study is to understand the current public discussion surrounding anterior cruciate ligament (ACL) injury prevention on social media and determine factors that influence levels of public engagement. METHODS: We performed a qualitative and quantitative cross-sectional analysis of ACL injury prevention techniques discussed on social media via the Twitter application programming interface (API). The Twitter API was queried from inception to May 2023 using keywords related to ACL injury and prevention. We conducted a thematic analysis of the posts and performed a sentiment analysis using natural language processing. A multivariable regression model was used to identify metadata that predicted higher engagement (media, links, tagging, hashtags). RESULTS: A subset of 1823 unique posts was analyzed from 1701 unique accounts. Most posts were raising awareness about ACL injury prevention (n = 733, 40.2%), followed by opinions on the topic (n = 390, 21.4%), specific prevention techniques (n = 289, 15.9%), personal experiences (n = 272, 14.9%), and research (n = 139, 7.6%). The majority consisted of posts from patients or caregivers (n = 948, 55.7%), whereas healthcare providers accounted for 14.7% of posts. Posts containing media increased Tweet engagement count by an average of 6.1 (95% CI 2.8 to 9.4, p = 0.00033) and posts discussing personal opinions increased engagement by 6.7 (95% CI 3.5 to 9.8, p = 0.00004). On sentiment analysis of all included Tweets, 822 (45.1%) posts were positive, 309 (17.0%) were negative, and 692 (38.0%) were neutral. Sentiments expressed in posts related to ACL prevention were 2.8 times more negative compared to those discussing raising awareness. CONCLUSIONS: There is active discussion about ACL injury prevention on Twitter. The use of visual media increased public engagement. We identified a potential knowledge gap between the available prevention techniques and the perspectives of athletes, highlighting the need for healthcare professionals to enhance their engagement with ACL injury prevention on social media. LEVEL OF EVIDENCE: III.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".