ArticleHeliyon2023
Identifying drivers of COVID-19 vaccine sentiments for effective vaccination policy.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed, 4 citations in OpenAlex.
No citing paper in PubMed yet.
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 at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic has had far-reaching consequences globally, including a significant loss of lives, escalating unemployment rates, economic instability, deteriorating mental well-being, social conflicts, and even political discord. Vaccination, recognized as a pivotal measure in mitigating the adverse effects of COVID-19, has evoked a diverse range of sentiments worldwide. In particular, numerous users on social media platforms have expressed concerns regarding vaccine availability and potential side effects. Therefore, it is imperative for governmental authorities and senior health policy strategists to gain insights into the public's perspectives on vaccine mandates in order to effectively implement their vaccination initiatives. Despite the critical importance of comprehending the underlying factors influencing COVID-19 vaccine sentiment, the existing literature offers limited research studies on this subject matter. This paper presents an innovative methodology that harnesses Twitter data to extract sentiment pertaining to COVID-19 vaccination through the utilization of Artificial Intelligence techniques such as sentiment analysis, entity detection, linear regression, and logistic regression. The proposed methodology was applied and tested on live Twitter feeds containing COVID-19 vaccine-related tweets, spanning from February 14, 2021, to April 2, 2023. Notably, this approach successfully processed tweets in 45 languages originating from over 100 countries, enabling users to select from an extensive scenario space of approximately 3.55 × 10
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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.