Health Tweets: An Exploration of Health Promotion on Twitter
Bibliographic record
Abstract
Twitter® is a popular microblogging site that allows users to disseminate information in 140 characters of text or less. A review of literature indicated that, to date, there has been little inquiry into the health based discussions conceptualized and enacted within and among Twitter users. Methods for this qualitative study included a directed content analysis, guided by the Public Health Agency of Canada's Determinant of Health (DOH) framework was completed to explore health based discussions on Twitter. A 24-hour cross-section of tweets (N=2400) containing the word or hashtag 'health' were collected for analysis. Findings revealed predominant themes of health services, personal health practices, and education. Many of the tweeted messages reflected existing political and social issues publicized within the global mass media. This study also considered the evolving dynamic behind the conceptualization of health and how it is co-constructed through news media, advertising, and social network technologies. Discussion of the emerging themes and implications for practice are presented.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".