Public perception of COVID-19 vaccines through analysis of Twitter content and users

Abstract

Background: With the global continuation of the COVID-19 pandemic, the large-scale administration of a SARS-CoV-2 vaccine is crucial to achieve herd immunity and curtail further spread of the virus, but success is contingent on public understanding and vaccine uptake. We aim to understand public perception about vaccines for COVID-19 through the wide-scale, organic discussion on Twitter.

Methods: This cross-sectional observational study included Twitter posts matching the search criteria (('covid*' OR 'coronavirus') AND 'vaccine') posted during vaccine development from February 1st through December 11th, 2020. These COVID-19 vaccine related posts were analyzed with topic modeling, sentiment and emotion analysis, and demographic inference of users to provide insight into the evolution of public attitudes throughout the study period.

Findings: We evaluated 2,287,344 English tweets from 948,666 user accounts. Individuals represented 87.9 % (n = 834,224) of user accounts. Of individuals, men (n = 560,824) outnumbered women (n = 273,400) by 2:1 and 39.5 % (n = 329,776) of individuals were ≥40 years old. Daily mean sentiment fluctuated congruent with news events, but overall trended positively. Trust, anticipation, and fear were the three most predominant emotions; while fear was the most predominant emotion early in the study period, trust outpaced fear from April 2020 onward. Fear was more prevalent in tweets by individuals (26.3 % vs. organizations 19.4 %; p < 0.001), specifically among women (28.4 % vs. males 25.4 %; p < 0.001). Multiple topics had a monthly trend towards more positive sentiment. Tweets comparing COVID-19 to the influenza vaccine had strongly negative early sentiment but improved over time.

Interpretation: This study successfully explores sentiment, emotion, topics, and user demographics to elucidate important trends in public perception about COVID-19 vaccines. While public perception trended positively over the study period, some trends, especially within certain topic and demographic clusters, are concerning for COVID-19 vaccine hesitancy. These insights can provide targets for educational interventions and opportunity for continued real-time monitoring.

Keywords: COVID-19; COVID-19 vaccines; Demographic inference; Natural language processing; Public opinion; SARS-CoV-2; Sentiment analysis; Social media; Topic modeling; Twitter; Vaccination; Vaccination refusal; Vaccine; Vaccine hesitancy.

Fig. 1

Word cloud of top 300 words related to COVID-19 and vaccine. Larger fonts represent higher frequency in the corpus after preprocessing text.

Fig. 2

. a–d. (a) Mean sentiment polarity shown by day (as points) and by week (as a dashed line). Each tweet was labeled as primarily negative (−1), neutral (0), or positive (1). (b) Mean weekly polarity stratified by individual versus organization. (c) Mean weekly polarity stratified by gender for individual accounts. (d) Mean weekly polarity stratified by age more or <40 years than for individual accounts.

Fig. 3

. a–d. Percent of tweets with primary emotion per month (a) overall, (b) stratified by individual versus organization, (c) stratified by gender for individual accounts, and (d) stratified by age more or <40 years than for individual accounts. Only tweets with a predominant primary emotion (n = 1,489,027) are included.

Fig. 4

Heat map showing mean sentiment by month for each topic. Note that a tweet can include multiple topics.