Twitter and traumatic brain injury: A content and sentiment analysis of tweets pertaining to sport-related brain injury
Bibliographic record
Abstract
OBJECTIVES: Sport-related traumatic brain injuries are a significant public health burden, with hundreds of thousands sustained annually in North America. While sports offer numerous physical and social health benefits, traumatic brain injuries such as concussion can seriously impact a player's life, athletic career, and sport enjoyment. The culture in many sports encourages winning at all costs, placing athletes at risk for traumatic brain injuries. As social media has become a central part of everyday life, the content of users' messages often reflects the prevailing culture related to a particular event or health issue. METHODS: tweets related to traumatic brain injuries in sports collected during June and July 2013. RESULTS: We identified five major themes. Users tweeted about personal traumatic brain injuries experiences, reported traumatic brain injuries in professional athletes, shared research about sport-related concussions, and discussed policy and safety in injury prevention, such as helmet use. We identified mixed perceptions of and sentiment toward traumatic brain injuries in sports: both an understanding that brain injuries are serious and disregard for activities that might reduce the public burden of traumatic brain injuries were prevalent in our Twitter analysis. CONCLUSION: While the scientific and medical community considers a concussion a form of traumatic brain injuries, our study demonstrates a misunderstanding of this fact among the public. In our current digital age, social media can provide useful insight into the culture around a health issue, facilitating implementation of prevention and treatment strategies.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".