The Impact of Omicron Variant in Vaccine Uptake in South Africa
Bibliographic record
Abstract
Abstract The first report of the Omicron variant triggered a lot of emotions towards vaccination in South Africa. These emotions are mostly expressed on social media such as Twitter. The result could weaken the confidence level of users even before they are vaccinated. Identifying these emotions and how they change before and during the Omicron variant can help, in understanding the dynamics in citizens' behaviour, towards vaccination for health policy-making. In this study, 23,000 vaccine-related Twitter posts were collected in South Africa, from 1 October 2021 to 15 January 2022 using Natural Language Processing techniques. The emotional classification of Twitter posts and their associated intensities were achieved using the Text2emotion pre-trained model. The results were validated using Naive Bayes with an accuracy of 76%, Logistic Regression (91%), Support Vector Machines (84%), Decision Tree (82%), and K-Nearest Neighbours (72%). The number of tweets significantly positively correlated with the increase in vaccination across all South African provinces (Corr<=0.532, P<=0.003) except Northern Cape province. The emotional intensities for vaccine-related posts showed a strong association with the increase in vaccination during Omicron (P<0.04) in Eastern Cape, Gauteng, Limpopo, and North West provinces than other provinces. The comparison of the intensities of the emotional classes differed across provinces before and during Omicron. The result of this research showed that social media data can be used to complement existing data, in understanding and predicting the dynamics in citizens' emotional behaviour towards vaccination, during a new COVID-19 variant or future outbreaks. The result could also inform health policy in planning, control, and management of provinces identified with vaccine hesitancy. It can also serve as a template or reference for related future academic research.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".