Exploring Sub-Saharan Africa’s Communication of COVID-19-Related Health Information on Social Media
Bibliographic record
Abstract
Abstract Social media presents a robust stage for disseminating time-sensitive information that is needed during a public health disease of global concern such as COVID-19. This study finds out how the 23 anglophone Sub-Saharan African countries’ national health ministries and infectious disease agencies disseminated COVID-19 related information through their social media accounts within the first three months after the declaration of COVID-19 as a pandemic by the World Health Organization. COVID-19 related qualitative and quantitative data types were collected from the social media accounts of the surveyed national health ministries and agencies for analysis. Over 86% of the African countries had presence on social media; Facebook was the most popular, though Twitter contained more posts. One of the credibility issues that was noticed is that most of the health ministries’ and agencies’ social media accounts were unverified and access to the social media accounts was not provided on most of their official websites. Information dissemination became more deliberate and increased significantly after the announcement of the fist cases of COVID-19 in the countries under review. Awareness creation, updates and news constituted the major categories of information that were disseminated, mostly in the form of derivative social media information before the announcement of the first COVID-19 case in the surveyed African countries. Campaigns against misinformation were barely undertaken by most of the countries. Strategies used by some countries included the employment of social media influencers and creation of content in local languages. Strategies that include development of health information content that targets different groups in African societies and the inclusion of elderly in the community and religious leaders as non-state actors in health information communication were recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".