THE INFLUENCE OF COVID-19 CONSPIRACY NARRATIVES ON VACCINE UPTAKE IN THE AGE OF SOCIAL MEDIA
Bibliographic record
Abstract
Following the outbreak of the novel COVID-19 virus in Wuhan City, China in 2019 and the subsequent metamorphose into a global pandemic in the first quarter of 2020; the world was thrown into frenzy due to information overload about the cause, nature, origin, aim, effect and likely solution to the virus. There was a preponderance of conspiracy theories from all quarters: health, academic, religion, politics, cosmology and mythology about the subject matter. The media, especially social media played a huge role to fan the embers of the theories. Four years on from the outbreak, a lot of the propositions have been proven while many have become obsolete and others have been discovered as false alarm. In addition, globally the fight against the pandemic has moved to the vaccination stage. Accordingly, this study sought to find out the extent and influence of the different conspiracy theories on the vaccination campaigns. 393 adult and educationally advanced residents of Uyo, the capital of Akwa Ibom State, Nigeria were sampled via an online Google questionnaire. The questionnaire was purposefully distributed through social media platforms such as WhatsApp, Facebook Messenger and Telegram etc. Findings from the study revealed first, high level of exposure to conspiracy theories about COVID-19 on social media. Second, despite the high level of exposure to such fake news on the subject matter, the exposure did not influence the acceptance or otherwise of the COVID-19 vaccines by the respondents. The study recommended among other things, promotion of media literacy education among the citizenry and strengthening of legal framework to detect and prosecute erring members of society or who originate fake news.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".