Using Twitter to Understand the Effects of the Cameroon Anglophone Crisis on Social Determinants of Health
Bibliographic record
Abstract
Insufficient opportunities to collect data on public health exist in armed conflict regions. Increased use of social media during war and conflict has allowed for data collection in situations where information is usually difficult to obtain. In this study, Twitter, a public social media platform, was used as a source of data and information to gain insight into how the Cameroon Anglophone Crisis impacts public health in the population. Our findings revealed that Twitter was being used to share information and call for action. Analysis of tweets revealed 8 distinct themes, which illustrated the impact of the crisis on the social determinants of health: neglect from government related to the social determinants of health; education; loss of employment; increased poverty; housing and homelessness; social exclusion and oppression; women and gender inequality; and health services. This study provides insight into the significant impact on public health in Cameroon caused by the Anglophone Crisis, and demonstrates the potential benefits of social media for gathering information about public health in crisis situations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".