Public Perceptions of COVID-19 Vaccine Information: Quantitative Analyses of Survey and Twitter Data in the United States
Bibliographic record
Abstract
ABSTRACTObjectives: We aimed to understand the public perception of COVID-19 vaccines using survey and Twitter data. For the survey study, we focused on examining the COVID-19 vaccine perspectives of the rural population in the Central Valley of California, which was predominantly Latinx. Specifically, we looked at the level of trust in the source and content of the vaccine information they received, their view of the safety and effectiveness of vaccines, and their accessibility to vaccines and information at the time when vaccines were readily available to the public. For the Twitter study, we focused on metropolitan and nonmetropolitan communities in the United States and examined tweet sentiment and emotion scores in the early stage of the pandemic and through the public release of the first COVID-19 vaccines. Methods: For the survey data, a total of 900 survey responses were collected in two rural counties in the State of California from March 30 to April 25, 2021. The survey was offered via web and phone in English, Spanish, Punjabi, and Hmong. The respondents were asked about their perceptions of COVID-19 vaccines, messaging, and sources of information. For the Twitter data, we used 127,648 tweets for the analysis after data cleaning, reverse geocoding of tweets, and assigning geographical designations to compare public perception between metropolitan and nonmetropolitan areas. We quantified public perception using the VADER (Valence Aware Dictionary for Sentiment Reasoning) lexicon to calculate sentiment scores and the NRCLex (National Research Council Canada Lexicon) to calculate emotions scores for the tweets. Next, we explored patterns in public perception of COVID-19 vaccines from March 11, 2020, to September 12, 2021. Then, we compared public perception between two separate periods (i.e., before and after December 11, 2020, when the Food and Drug Administration issued an emergency use authorization of Pfizer, the first COVID-19 vaccine). Results: In the survey approach, 41% of respondents were Latinx. The most frequent concerns noted for COVID-19 vaccine hesitancy were lack of confidence in the vaccine and the state and federal government (46-56%). However, complacency about the seriousness of the COVID-19 vaccines and disease (35%) and convenience or issues in access, travel time, and cost of vaccines (20%) were not associated with decisions regarding COVID-19 vaccination. In the Twitter approach, we found that public sentiment and emotion varied by geography though our findings did not significantly differ for metropolitan and nonmetropolitan residents. Fear was prevalent in the early times when COVID-19 was announced as a pandemic. However, this was quickly taken over by the emotion of trust later as the breakthroughs in COVID-19 vaccines were announced. Specifically, trust peaked on November 9, 2020, when Pfizer announced its vaccine was 90% effective. Then, around December 11, 2020, positive and negative tweet sentiments started diverging more clearly than the extreme sentiment fluctuations before this period.Conclusions: For urban or rural and metropolitan or nonmetropolitan communities, news and social media are potent outlets for health information and can significantly change the public’s perceptions about COVID-19 vaccines. The survey data shows rural residents in the Central Valley of California, predominantly Latinx, have high confidence or trust in healthcare providers, and the county public health department. However, approximately 40% of these rural residents were still unlikely to get vaccinated, similar to rural populations throughout the country. Recommendations to combat COVID-19 vaccine hesitancy amongst Latinx rural residents include leveraging trusted sources such as local doctors, family/friends, and local public health departments to encourage vaccination amongst this population. In addition, Twitter data shows that announcements from the public media or private institutions appear associated with the public’s perception of COVID-19 vaccines, such as the first news of the effectiveness of the Pfizer COVID-19 vaccine or the notice of blood clot issues caused by the Johnson & Johnson COVID-19 vaccine. Also, there was no significant difference in the mean sentiment or emotion scores between geographical distributions from March 2020 to September 2021. Overall, COVID-19 vaccine news appears to penetrate the public whether people are in urban or rural and metropolitan or nonmetropolitan communities.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".