Social Media Influencers and HIV/AIDS Awareness in Northern Nigerian Communities: A Comparative Study
Bibliographic record
Abstract
Social media influencers (SMIs) have become influential in shaping public health perceptions globally. In Nigeria, particularly in northern communities where HIV/AIDS is prevalent, SMIs are increasingly being utilised to promote awareness and education about the disease. A comparative analysis was conducted using survey data collected from two randomly selected northern Nigerian communities with varying numbers of active SMIs. Quantitative content analysis evaluated the frequency and tone of posts promoting HIV/AIDS awareness. Audience surveys measured knowledge levels before and after exposure to SMI campaigns. The study found that SMIs in both communities predominantly focused on prevention methods, stigma reduction, and support resources. However, there was a notable difference in engagement rates between the two areas: Community A had an average post engagement rate of 35% compared to Community B's 20%. Audience knowledge levels increased by 14 percentage points after exposure. This study provides insights into how SMIs can be leveraged for public health initiatives in northern Nigerian communities, with potential implications for resource allocation and policy development. Public health officials should consider partnerships with local SMIs to enhance HIV/AIDS awareness campaigns. Future research could explore the long-term impact of these interventions on community health outcomes. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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; both teacher heads agree on what is shown here.
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".