Evidence map›Paper›PMID 35578643›Full record

ArticleJMIR infodemiology

Public Opinion and Sentiment Before and at the Beginning of COVID-19 Vaccinations in Japan: Twitter Analysis.

Qian Niu, Junyu Liu, Masaya Kato, Yuki Shinohara, Natsuki Matsumura, Tomoki Aoyama, Momoko Nagai-Tanima

Abstract read
In one paragraph

Article in JMIR infodemiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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  13. Sentiment and emotion trends in nurses' tweets about the COVID-19 pandemic.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2022
    Article
  14. Article
  15. Article
  16. Article
  17. The Growing Impact of Natural Language Processing in Healthcare and Public Health.Inquiry : a journal of medical care organization, provision and financing
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Qian Niu *Department of Human Health Sciences Graduate School of Medicine Kyoto University Kyoto Japan.ORCID https://orcid.org/0000-0001-7139-384X
Junyu Liu *Department of Intelligence Science and Technology Graduate School of Informatics Kyoto University Kyoto Japan.ORCID https://orcid.org/0000-0001-6232-4199
Masaya KatoDepartment of Human Health Sciences Graduate School of Medicine Kyoto University Kyoto Japan.ORCID https://orcid.org/0000-0001-5589-2665
Yuki ShinoharaDepartment of Human Health Sciences Graduate School of Medicine Kyoto University Kyoto Japan.ORCID https://orcid.org/0000-0003-2083-3539
Natsuki MatsumuraDepartment of Human Health Sciences Graduate School of Medicine Kyoto University Kyoto Japan.ORCID https://orcid.org/0000-0002-3078-3419
Tomoki AoyamaDepartment of Human Health Sciences Graduate School of Medicine Kyoto University Kyoto Japan.ORCID https://orcid.org/0000-0003-3512-054X
Momoko Nagai-TanimaDepartment of Human Health Sciences Graduate School of Medicine Kyoto University Kyoto Japan.ORCID https://orcid.org/0000-0003-3972-8800

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: COVID-19 vaccines are considered one of the most effective ways for containing the COVID-19 pandemic, but Japan lagged behind other countries in vaccination in the early stages. A deeper understanding of the slow progress of vaccination in Japan can be instructive for COVID-19 booster vaccination and vaccinations during future pandemics. Objective: This retrospective study aims to analyze the slow progress of early-stage vaccination in Japan by exploring opinions and sentiment toward the COVID-19 vaccine in Japanese tweets before and at the beginning of vaccination. Methods: We collected 144,101 Japanese tweets containing COVID-19 vaccine-related keywords between August 1, 2020, and June 30, 2021. We visualized the trend of the tweets and sentiments and identified the critical events that may have triggered the surges. Correlations between sentiments and the daily infection, death, and vaccination cases were calculated. The latent dirichlet allocation model was applied to identify topics of negative tweets from the beginning of vaccination. We also conducted an analysis of vaccine brands (Pfizer, Moderna, AstraZeneca) approved in Japan. Results: The daily number of tweets continued with accelerating growth after the start of large-scale vaccinations in Japan. The sentiments of around 85% of the tweets were neutral, and negative sentiment overwhelmed the positive sentiment in the other tweets. We identified 6 public-concerned topics related to the negative sentiment at the beginning of the vaccination process. Among the vaccines from the 3 manufacturers, the attitude toward Moderna was the most positive, and the attitude toward AstraZeneca was the most negative. Conclusions: Negative sentiment toward vaccines dominated positive sentiment in Japan, and the concerns about side effects might have outweighed fears of infection at the beginning of the vaccination process. Topic modeling on negative tweets indicated that the government and policy makers should take prompt actions in building a safe and convenient vaccine reservation and rollout system, which requires both flexibility of the medical care system and the acceleration of digitalization in Japan. The public showed different attitudes toward vaccine brands. Policy makers should provide more evidence about the effectiveness and safety of vaccines and rebut fake news to build vaccine confidence.

Indexed as

COVID-19Japanlatent dirichlet allocationnatural language processingsentimentTwittervaccine

Identifiers

PMID35578643
PMCPMC9092950

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Registered trials

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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.