Breathing together: A global hashtag analysis of #LungHealth on platform X (formerly Twitter)
Bibliographic record
Abstract
Background: The X platform has gained popularity in healthcare, with posts among physicians increasing by 112% over five years. In the context of pulmonology, #LungHealth is used for engagement, spreading awareness, and disseminating information on respiratory diseases, yet its impact remains unexplored. Objective: We aim to analyze #LungHealth by measuring outreach and quantitative engagement metrics associated with #LungHealth. Methods: We conducted an analysis of #LungHealth posts over social media platform X (formerly Twitter) using Fedica, an analytic tool over 2 years from 1st May 2022 to 1st May 2024. This analysis studied quantitative metrics such as the number of posts, likes, views, user's geographical distribution, and co-occurring hashtags. Results: This hashtag analysis of #LungHealth for 2 years resulted in a total of 13,824 posts. The study showed that these posts were generated by 6100 active users producing over 80 million impressions. The posts garnered 1116 replies and 19,006 likes. Geographical distribution showed the posts spanning across 105 countries. The top 10 countries with the highest number of posts were the USA, India, UK, Canada, Australia, Nigeria, Belgium, Netherlands, Switzerland, and Kenya. Common co-occurring hashtags identified were #COPD, #COVID19, #Lungcancer, and #Asthma. The top 10 influencers consisted of six Indian media outlets, one health company, one educational institution, one Arabic media outlet, and one American journalist. Conclusion: This is the first study that provides a unique perspective on utilizing #LungHealth as a tool for global engagement in promoting lung health awareness and reaching various audiences on a global scale.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".