Public Perceptions of COVID-19 Vaccine Information: Quantitative Analyses of Survey and Twitter Data in the United States
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».