The Effect of Fear of Infection and Sufficient Vaccine Reservation Information on Rapid COVID-19 Vaccination in Japan: Evidence From a Retrospective Twitter Analysis

  1. Qian Niu
  2. Junyu Liu
  3. Masaya Kato
  4. Momoko Nagai-Tanima
  5. Tomoki Aoyama

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Abstract

The global public health and socioeconomic impacts of the COVID-19 pandemic have been substantial, rendering 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 rates in several months, although vaccine confidence in Japan is the lowest worldwide.

Objective

We attempted to find the reasons for rapid COVID-19 vaccination in Japan given its lowest vaccine confidence levels worldwide, through Twitter analysis. 

Methods

We downloaded COVID-19–related Japanese tweets from a large-scale public COVID-19 Twitter chatter data set within the timeline of February 1 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 and bigram token analysis and then calculated the cross-correlation and Pearson correlation coefficient (r) between the term frequency and daily vaccination cases. We then 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. Through n-gram token analysis, we discovered the top unigrams and bigrams over the whole period. In all the combinations of the top 6 unigrams, tweets with both keywords “reserve” and “venue” showed the largest correlation with daily vaccination cases (r=0.912; P<.001). On 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 “infect” was larger than that generated from topic “vaccine confidence.”

Conclusions

Our work indicates that awareness of the danger of COVID-19 might increase the willingness to get vaccinated. With a sufficient vaccine supply, effective delivery of vaccine reservation information may be an important factor for people to get vaccinated. We did not find evidence for increased vaccine confidence in Japan during the period of our study. We recommend policy makers to share accurate and prompt information about the infectious diseases and vaccination and to make efforts on smoother delivery of vaccine reservation information.

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  1. Version published to 10.2196/37466
  2. ScreenIT

    SciScore for 10.1101/2022.05.15.22275071: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We removed the pre-defined English, and Japanese stop words in the Python packages NLTK [28] and SpaCy [29], and tokenized all collected vaccine-related tweets using the Python package SpaCy into unigrams or bigrams for statistical analysis, as in the work of Kwok et al. [26].
    Python
    suggested: (IPython, RRID:SCR_001658)
    A Python package scikit-learn was used to determine the best number of topics.
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    We admit that our research might have some potential limitations: (1) the imbalance of the demographics of Twitter users in Japan [49] may cause bias in the results; (2) status of the user on a certain day (at home or not, other events on that day, etc.) may also bias the dataset [50]; (3) due to the lack of a reliable public model for sentiment analysis in the Japanese language, the cloud service AWS was used for sentiment analysis; (4) filtering keywords may include irrelevant or missing related tweets; (5) anti-vaccine tweets, especially rumors, were not distinguished or analyzed separately in this study. However, feature works can be combined with classical surveys to train the sentiment analysis model and model to distinguish rumors from tweets to overcome these limitations. This retrospective study aimed to determine the reasons for the fast vaccination process in Japan, which might be instructive for propelling worldwide vaccination towards infectious diseases. In conclusion, our work indicated that awareness of the danger of COVID-19 increased the willingness to be vaccinated; with a sufficient supply of vaccines, effective reservation information delivery might provide more opportunities for people to be vaccinated. Models measuring vaccine hesitancy might also need to add efficiency in delivering reservation information as a metric. Based on our findings, we recommend public health policymakers and the government to share fair and prompt information about the infect...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2022.05.15.22275071v1 on medRxiv
  4. Version published to 10.2196/preprints.37466