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Public Opinion and Sentiment Before and at the Beginning of COVID-19 Vaccinations in Japan: Twitter Analysis.
Niu, Qian; Liu, Junyu; Kato, Masaya; Shinohara, Yuki; Matsumura, Natsuki; Aoyama, Tomoki; Nagai-Tanima, Momoko.
  • Niu Q; Department of Human Health Sciences Graduate School of Medicine Kyoto University Kyoto Japan.
  • Liu J; Department of Intelligence Science and Technology Graduate School of Informatics Kyoto University Kyoto Japan.
  • Kato M; Department of Human Health Sciences Graduate School of Medicine Kyoto University Kyoto Japan.
  • Shinohara Y; Department of Human Health Sciences Graduate School of Medicine Kyoto University Kyoto Japan.
  • Matsumura N; Department of Human Health Sciences Graduate School of Medicine Kyoto University Kyoto Japan.
  • Aoyama T; Department of Human Health Sciences Graduate School of Medicine Kyoto University Kyoto Japan.
  • Nagai-Tanima M; Department of Human Health Sciences Graduate School of Medicine Kyoto University Kyoto Japan.
JMIR Infodemiology ; 2(1): e32335, 2022.
Article in English | MEDLINE | ID: covidwho-1951934
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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study Topics: Vaccines Language: English Journal: JMIR Infodemiology Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study Topics: Vaccines Language: English Journal: JMIR Infodemiology Year: 2022 Document Type: Article