Exploring HIV-AIDS interests in the MENA region using Internet based searches
Bibliographic record
Abstract
Due to the scarcity of official data on sexually transmitted diseases in the Middle East and North Africa region (MENA), it becomes important to seek alternative indications on the online information interests and possible spread of such diseases. This paper uses news stories from 10 Arabic media outlets, Wikipedia views, and data from Google Trends as well as social media on the HIV-AIDS epidemic. In order to investigate whether Internet searches are driven or influenced by media coverage, the correlation between media coverage and Internet searches is examined. The results indicate that there are very weak to moderate correlations between the two as media coverage of HIV-AIDS is not a good indicator of public attention. Data sources that are more accessible, like Google and Wikipedia searches and social media, can provide a better understanding of public information interests. Also, data retrieved from Google Trends in relation to the search terms “AIDS treatment” and “AIDS symptoms” provide important indicators on the top cities from which searches often originate. The findings of the study can aid health practitioners in identifying interest in and awareness of HIV-AIDS in the MENA region.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".