#Globalhealth Twitter Conversations on #Malaria, #HIV, #TB, #NCDS, and #NTDS: a Cross-Sectional Analysis
Bibliographic record
Abstract
BACKGROUND: Advocates use the hashtag #GlobalHealth on Twitter to draw users' attention to prominent themes on global health, to harness their support, and to advocate for change. OBJECTIVES: We aimed to describe #GlobalHealth tweets pertinent to given major health issues. METHODS: Tweets containing the hashtag #GlobalHealth (N = 157,951) from January 1, 2014, to April 30, 2015, were purchased from GNIP Inc. We extracted 5 subcorpora of tweets, each with 1 of 5 co-occurring disease-specific hashtags (#Malaria, #HIV, #TB, #NCDS, and #NTDS) for further analysis. Unsupervised machine learning was applied to each subcorpus to categorize the tweets by their underlying topics and obtain the representative tweets of each topic. The topics were grouped into 1 of 4 themes (advocacy; epidemiological information; prevention, control, and treatment; societal impact) or miscellaneous. Manual categorization of most frequent users was performed. Time zones of users were analyzed. FINDINGS: In the entire #GlobalHealth corpus (N = 157,951), there were 40,266 unique users, 85,168 retweets, and 13,107 unique co-occurring hashtags. Of the 13,087 tweets across the 5 subcorpora with co-occurring hashtag #malaria (n = 3640), #HIV (n = 3557), #NCDS (noncommunicable diseases; n = 2373), #TB (tuberculosis; n = 1781), and #NTDS (neglected tropical diseases; n = 1736), the most prevalent theme was prevention, control, and treatment (4339, 33.16%), followed by advocacy (3706, 28.32%), epidemiological information (1803, 13.78%), and societal impact (1617, 12.36%). Among the top 10 users who tweeted the highest number of tweets in the #GlobalHealth corpus, 5 were individual professionals, 3 were news media, and 2 were organizations advocating for global health. The most common users' time zone was Eastern Time (United States and Canada). CONCLUSIONS: This study highlighted the specific #GlobalHealth Twitter conversations pertinent to malaria, HIV, tuberculosis, noncommunicable diseases, and neglected tropical diseases. These conversations reflect the priorities of advocates, funders, policymakers, and practitioners of global health on these high-burden diseases as they presented their views and information on Twitter to their followers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".