ArticleJournal of environmental and public health2022
"Before Dawn," Listening to the Voices of Social Media: A Study on the Public's Response to the COVID-19 Vaccine.
Article in Journal of environmental and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Willingness to receive cross-border human papillomavirus vaccination among Chinese male university students: a cross-sectional study with statistical indirect-association analysis.Frontiers in public health · 2026Article
- BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.Frontiers in public health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic is a worldwide catastrophe. In the absence of an effective drug, one effective measure to pull the pandemic to the end is herd immunity by taking vaccines, while the hesitation and anti-attitude from social media affect the vaccination. This makes it crucial to evaluate the text data about the COVID-19 vaccine from tweets. The period for data used in this study is 1 Aug to 31 Oct, 2020, since it is just before promoting the use when public reactions to the COVID-19 vaccine can influence their subsequent vaccination behavior. In this study, we used the latent Dirichlet allocation (LDA) topic model and sentiment analysis to explore public reactions to the COVID-19 vaccine. The results indicate that the public discussion could be divided into 11 topics, which could be further summarized into four different themes: (1) concerns about COVID-19; (2) concerns about vaccine development, production, and distribution; (3) how to control the COVID-19 before obtaining the vaccine; and (4) concerns about information of vaccine safety and efficacy. It can be concluded that to a large extent, public reactions to vaccines are dominated by positive sentiment. Specifically, the politicization of the vaccine approval process, suspension of vaccine trials, and measures to control COVID-19 tend to trigger negative public sentiment; whereas information related to successful vaccine development and availability enhances positive public sentiment. These findings help us understand public reactions to the COVID-19 vaccine, uncover potential factors that may influence vaccination behavior, and help policymakers understand public opinion about the COVID-19 vaccine and develop rational and effective policies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.