Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Feb 22, 2022
Open Peer Review Period: Feb 22, 2022 - Apr 19, 2022
Date Accepted: May 30, 2022
Date Submitted to PubMed: Jun 1, 2022
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Fear of Infection and Sufficient Vaccine Reservation Information Might Drive Rapid Coronavirus Disease 2019 Vaccination in Japan: Evidence from Twitter Analysis
ABSTRACT
Background:
The global public health and socioeconomic impacts of coronavirus disease 2019 (COVID-19) have been substantial, making herd immunity by COVID-19 vaccination an important factor for protecting people and retrieving the economy. Among all the countries, Japan became one of the countries with the highest COVID-19 vaccination rate in several months, although the vaccine confidence in Japan is the lowest worldwide.
Objective:
We attempt to find the reasons for the rapid COVID-19 vaccination in Japan using Twitter analysis.
Methods:
We downloaded COVID-19 related Japanese tweets from a large-scale public COVID-19 Twitter chatter dataset within the timeline of February 1, 2021 and September 30, 2021. The daily number of vaccination cases was collected from the official website of the Prime Minister’s Office of Japan. After preprocessing, we applied unigram token analysis and calculated the Pearson correlation coefficient (r) between the term frequency and daily vaccination cases. Then we identified vaccine sentiments and emotions of tweets and used the topic modeling to look deeper into the dominant emotions.
Results:
We selected 190,697 vaccine-related tweets after filtering. By unigram token analysis, we discovered the top unigrams over the whole period. In all the combinations of the top six unigrams, tweets with both keywords “reserve” and “venue” showed the largest r = 0.912 (P < 0.001) with the daily vaccination cases. In sentiment analysis, negative sentiment overwhelmed positive sentiment, and fear was the dominant emotion across the period. For the latent Dirichlet allocation model on tweets with fear emotion, the two topics were identified as “infect” and “vaccine confidence”. The expectation of the number of tweets generated from topic “infection” was larger than “vaccine confidence.”
Conclusions:
Our work indicated that awareness of the danger of COVID-19 might increase the willingness to get vaccinated; With sufficient vaccine supply, effective vaccine reservation information delivery may be an important factor for people to get vaccinated; We didn’t find evidence for increased vaccine confidence in Japan during the period in our research.
Citation
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Copyright
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