Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study
Bibliographic record
Abstract
BACKGROUND: The global onset of COVID-19 has resulted in substantial public health and socioeconomic impacts. An immediate medical breakthrough is needed. However, parallel to the emergence of the COVID-19 pandemic is the proliferation of information regarding the pandemic, which, if uncontrolled, cannot only mislead the public but also hinder the concerted efforts of relevant stakeholders in mitigating the effect of this pandemic. It is known that media communications can affect public perception and attitude toward medical treatment, vaccination, or subject matter, particularly when the population has limited knowledge on the subject. OBJECTIVE: This study attempts to systematically scrutinize media communications (Google News headlines or snippets and Twitter posts) to understand the prevailing sentiments regarding COVID-19 vaccines in Africa. METHODS: A total of 637 Twitter posts and 569 Google News headlines or descriptions, retrieved between February 2 and May 5, 2020, were analyzed using three standard computational linguistics models (ie, TextBlob, Valence Aware Dictionary and Sentiment Reasoner, and Word2Vec combined with a bidirectional long short-term memory neural network). RESULTS: Our findings revealed that, contrary to general perceptions, Google News headlines or snippets and Twitter posts within the stated period were generally passive or positive toward COVID-19 vaccines in Africa. It was possible to understand these patterns in light of increasingly sustained efforts by various media and health actors in ensuring the availability of factual information about the pandemic. CONCLUSIONS: This type of analysis could contribute to understanding predominant polarities and associated potential attitudinal inclinations. Such knowledge could be critical in informing relevant public health and media engagement policies.
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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 | 0.001 | 0.014 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".