Sexual transmission of Zika virus on Twitter: A depoliticised epidemic
Bibliographic record
Abstract
During global health crises, different narratives regarding infectious disease epidemics circulate in traditional media (e.g. news agencies, television channels) and social media. Our study investigated the narratives related to sexual transmission of Zika virus that circulated on Twitter during a public health emergency and analyzed the relationship between information on Twitter and on traditional media. We examined 10,748 tweets posted during the peaks of Twitter activity between January and March 2016. Posts in English, Spanish, French, and Portuguese and websites linked to tweets were manually reviewed and analyzed thematically. During the study period, there were three peaks of Twitter activity related to the sexual transmission of Zika. Most tweets in the first peak (n = 412) had humorous/sarcastic content (55%). Most tweets in the second and third peaks (n = 5,154 and n = 5,182, respectively) disseminated information (>93%). Across languages, textual and visual content on the websites were predominantly placed online by traditional media and highlighted epidemiological narratives published by public health agencies, with little or no mention of the concerns or experiences of individuals most affected by Zika. Prioritising epidemiological/clinical aspects of epidemics may have a depoliticising effect and contribute to overlooking socio-economic determinants of the Zika epidemic and issues related to reproductive justice.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".