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

Abstract

Background: The global public health and socioeconomic impacts of COVID-19 have been substantial. To achieve herd immunity, widespread use of the vaccine is required, and it is therefore critical for government and public health agencies to understand public perceptions of the vaccine to help sustain subsequent vaccinations. Objective: This study aims to explore the opinions and sentiments of tweets about COVID-19 vaccination among Twitter users in Japan, both before and at the beginning of the COVID-19 vaccination program. Methods: We collected 144,101 Japanese tweets containing COVID-19 vaccine-related keywords from Japanese Twitter users between August 1, 2020, and June 30, 2021. Specifically, we identified temporal changes in the number of tweets and key events that triggered a surge in the number of tweets. In addition, we performed sentiment analysis, and calculated the correlation between different sentiments and the number of deaths, infections, and vaccinations. We also built latent Dirichlet allocation (LDA) topic models to identify commonly discussed topics in a large sample of tweets. We also provided a word cloud of high-frequency unigram and bigram tokens as additional evidence, and conducted further analysis on three different vaccine brands. Results: The overall number of tweets has continued to increase since the start of mass vaccination in Japan. Sentiments were generally neutral, but negative sentiment was more significant than positive sentiment. Before and after the first vaccination in Japan, the correlations of negative/positive sentiment with death, infection, and vaccination cases changed significantly. Public concerns revolved around three themes: information on vaccine reservations and vaccinations in Japan; infection and mutation of COVID-19 in Japan; and prevention measures, vaccine development and supply, and vaccination status in other countries. Furthermore, public attention to the three brands of vaccines has a temporal shift as clinical trials move forward. Conclusions: The number of tweets and changes in sentiment are primarily driven by major news events in relation to the COVID-19 vaccine, with negative sentiments dominating positive sentiments overall. Death and infection cases correlated significantly with negative sentiments, but the correlation fell after vaccinations began as morbidity and mortality decreased. The attention of the public to different vaccine brands had a temporal change during their clinical trial process, and although the discussion points are slightly different, the core remains effective and secure.

Trust and Engagement on Twitter During the Management of COVID-19 Pandemic: The Effect of Gender and Position

“…The results of comparing positive, negative, and neutral sentiments before and after vaccination in Canada and the US is given in Figure 1 . Generally, neutral was the majority sentiment which is consistent with the results of a similar study in Japan (Niu et al, 2021 ). The negative sentiment was more in Canada and the positive sentiment was more in the US.…”

Emotion Analysis of Writers and Readers of Japanese Tweets on Vaccinations

“…The work most similar to ours is that of Niu et al (2022), who perform sentiment analysis of COVID-19-related Japanese Tweets; investigate the correlation of the mined sentiments with infections, deaths, and vaccinations; and conduct additional analyses comparing multiple vaccine brands. Our study differs from this by extracting emotions rather than sentiments from both writers and readers, and comparing these mined features to vaccinations, vaccinated people, and fully vaccinated people.…”

“…The wealth of information available on social networking services such as Twitter already makes them a popular source for mining sentiment and emotions in other areas such as politics (Bose et al, 2019) and consumerism (Rathan et al, 2018). Social media information extraction has seen continued research during the COVID-19 pandemic, with multiple works specifically seeking to mine sentiments and emotions surrounding the pandemic and vaccination (Boon-Itt and Skunkan, 2020;Sakti et al, 2021;Aygün et al, 2021;Niu et al, 2022).…”

Sentiment analysis of public sensitivity to COVID-19 vaccines on twitter by majority voting classifier-based machine learning

“…Benzer kapsamda Niu vd. 'nin [28] Japonya kapsamında gerçekleştirdikleri çalışmalarında büyük ölçekli aşıların başlangıcında ve öncesinde, Japonca tweetler üzerinde COVID-19 aşısına yönelik görüş ve duyarlılığı araştırmayı amaçlamışlardır. Elde edilen sonuçlar neticesinde tweetlerin yaklaşık %85 gibi büyük bir oran ile nötr duyguları yansıtırken geri kalanların büyük bir çoğunluğun negatif duygu polaritesine sahip olduğu ortaya konmuştur.…”