Patterns of diverse and changing sentiments towards COVID-19 vaccines: a sentiment analysis study integrating 11 million tweets and surveillance data across over 180 countries. (23rd February 2023)

• Record Type: Journal Article
• Authors: Wang, Hanyin; Li, Yikuan; Hutch, Meghan R; Kline, Adrienne S; Otero, Sebastian; Mithal, Leena B; Miller, Emily S; Naidech, Andrew; Luo, Yuan
• Is Part Of: Journal of the American Medical Informatics Association. Volume 30:Number 5(2023)
• Page Start: 923
• Page End: 931
• Publication Date: 2023-02-23
• Subjects: COVID-19 -- vaccine -- sentiment analysis -- deep learning; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocad029
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objectives: Vaccines are crucial components of pandemic responses. Over 12 billion coronavirus disease 2019 (COVID-19) vaccines were administered at the time of writing. However, public perceptions of vaccines have been complex. We integrated social media and surveillance data to unravel the evolving perceptions of COVID-19 vaccines. Materials and Methods: Applying human-in-the-loop deep learning models, we analyzed sentiments towards COVID-19 vaccines in 11 211 672 tweets of 2 203 681 users from 2020 to 2022. The diverse sentiment patterns were juxtaposed against user demographics, public health surveillance data of over 180 countries, and worldwide event timelines. A subanalysis was performed targeting the subpopulation of pregnant people. Additional feature analyses based on user-generated content suggested possible sources of vaccine hesitancy. Results: Our trained deep learning model demonstrated performances comparable to educated humans, yielding an accuracy of 0.92 in sentiment analysis against our manually curated dataset. Albeit fluctuations, sentiments were found more positive over time, followed by a subsequence upswing in population-level vaccine uptake. Distinguishable patterns were revealed among subgroups stratified by demographic variables. Encouraging news or events were detected surrounding positive sentiments crests. Sentiments in pregnancy-related tweets demonstrated a lagged pattern compared with the general population, with delayed vaccine uptake trends. Feature analysis detected hesitancies stemmed from clinical trial logics, risks and complications, and urgency of scientific evidence. Discussion: Integrating social media and public health surveillance data, we associated the sentiments at individual level with observed populational-level vaccination patterns. By unraveling the distinctive patterns across subpopulations, the findings provided evidence-based strategies for improving vaccine promotion during pandemics.