ArticleComputer methods and programs in biomedicine2022
Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout.
Article in Computer methods and programs in biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- When Infodemic Meets Epidemic: Systematic Literature Review.JMIR public health and surveillance · 2025Pooled it
- Article
- Investigation of Public Acceptance of Misinformation Correction in Social Media Based on Sentiment Attributions: Infodemiology Study Using Aspect-Based Sentiment Analysis.Journal of medical Internet research · 2024Article
- Users' Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts.Journal of medical Internet research · 2023Article
- Analysis of Differences in User Groups and Post Sentiment of COVID-19 Vaccine Hesitators in Chinese Social-Media Platforms.Healthcare (Basel, Switzerland) · 2023Article
- Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review.Journal of medical Internet research · 2023Article
- COVID-19 vaccination perception and outcome: society sentiment analysis on twitter data in India.Social network analysis and mining · 2023Article
- Public Discourse Surrounding Suicide during the COVID-19 Pandemic: An Unsupervised Machine Learning Analysis of Twitter Posts over a One-Year Period.International journal of environmental research and public health · 2022Article
- Article
- Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions.Computational intelligence and neuroscience · 2022Article
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8 authors.
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Abstract
BACKGROUND AND
objectiveSocial media sentiment analysis based on Twitter data can facilitate real-time monitoring of COVID-19 vaccine-related concerns. Thus, the governments can adopt proactive measures to address misinformation and inappropriate behaviors surrounding the COVID-19 vaccine, threatening the success of the national vaccination campaign. This study aims to identify the correlation between COVID-19 vaccine sentiments expressed on Twitter and COVID-19 vaccination coverage, case increase, and case fatality rate in Indonesia.
methodsWe retrieved COVID-19 vaccine-related tweets collected from Indonesian Twitter users between October 15, 2020, to April 12, 2021, using Drone Emprit Academic (DEA) platform. We collected the daily trend of COVID-19 vaccine coverage and the rate of case increase and case fatality from the Ministry of Health (MoH) official website and the KawalCOVID19 database, respectively. We identified the public sentiments, emotions, word usage, and trend of all filtered tweets 90 days before and after the national vaccination rollout in Indonesia.
resultsUsing a total of 555,892 COVID-19 vaccine-related tweets, we observed the negative sentiments outnumbered positive sentiments for 59 days (65.50%), with the predominant emotion of anticipation among 90 days of the beginning of the study period. However, after the vaccination rollout, the positive sentiments outnumbered negative sentiments for 56 days (62.20%) with the growth of trust emotion, which is consistent with the positive appeals of the recent news about COVID-19 vaccine safety and the government's proactive risk communication. In addition, there was a statistically significant trend of vaccination sentiment scores, which strongly correlated with the increase of vaccination coverage (r = 0.71, P<.0001 both first and second doses) and the decreasing of case increase rate (r = -0.70, P<.0001) and case fatality rate (r = -0.74, P<.0001).
conclusionsOur results highlight the utility of social media sentiment analysis as government communication strategies to build public trust, affecting individual willingness to get vaccinated. This finding will be useful for countries to identify and develop strategies for speed up the vaccination rate by monitoring the dynamic netizens' reactions and expression in social media, especially Twitter, using sentiment analysis.
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