Public attitudes toward COVID-19 vaccines on English-language Twitter: A sentiment analysis

Liu, Siru; Liu, Jialin

doi:10.1016/j.vaccine.2021.08.058

Cited by 111 publications

(102 citation statements)

References 29 publications

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“…Yin et al [14] proposed a novel behavioral dynamics model on Weibo messages to analyze vaccine acceptance in China, and they demonstrated that Chinese individuals were inclined to be positive about side effects over time. Furthermore, many studies explored tweets related to COVID-19 vaccines to understand the evolution of public concerns and sentiments in different regions [15][16][17]. These studies mainly revealed that public concerns and sentiments on COVID-19 vaccines fluctuated with time and geography, and had strong correlations with some major events about COVID-19 vaccines.…”

Section: Literature Reviewmentioning

confidence: 99%

The Evolution and Disparities of Online Attitudes Toward COVID-19 Vaccines: Year-long Longitudinal and Cross-sectional Study

Zhang¹,

Li²,

Liu³

et al. 2022

J Med Internet Res

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Background Due to the urgency caused by the COVID-19 pandemic worldwide, vaccine manufacturers have to shorten and parallel the development steps to accelerate COVID-19 vaccine production. Although all usual safety and efficacy monitoring mechanisms remain in place, varied attitudes toward the new vaccines have arisen among different population groups. Objective This study aimed to discern the evolution and disparities of attitudes toward COVID-19 vaccines among various population groups through the study of large-scale tweets spanning over a whole year. Methods We collected over 1.4 billion tweets from June 2020 to July 2021, which cover some critical phases concerning the development and inoculation of COVID-19 vaccines worldwide. We first developed a data mining model that incorporates a series of deep learning algorithms for inferring a range of individual characteristics, both in reality and in cyberspace, as well as sentiments and emotions expressed in tweets. We further conducted an observational study, including an overall analysis, a longitudinal study, and a cross-sectional study, to collectively explore the attitudes of major population groups. Results Our study derived 3 main findings. First, the whole population’s attentiveness toward vaccines was strongly correlated (Pearson r=0.9512) with official COVID-19 statistics, including confirmed cases and deaths. Such attentiveness was also noticeably influenced by major vaccine-related events. Second, after the beginning of large-scale vaccine inoculation, the sentiments of all population groups stabilized, followed by a considerably pessimistic trend after June 2021. Third, attitude disparities toward vaccines existed among population groups defined by 8 different demographic characteristics. By crossing the 2 dimensions of attitude, we found that among population groups carrying low sentiments, some had high attentiveness ratios, such as males and individuals aged ≥40 years, while some had low attentiveness ratios, such as individuals aged ≤18 years, those with occupations of the 3rd category, those with account age <5 years, and those with follower number <500. These findings can be used as a guide in deciding who should be given more attention and what kinds of help to give to alleviate the concerns about vaccines. Conclusions This study tracked the year-long evolution of attitudes toward COVID-19 vaccines among various population groups defined by 8 demographic characteristics, through which significant disparities in attitudes along multiple dimensions were revealed. According to these findings, it is suggested that governments and public health organizations should provide targeted interventions to address different concerns, especially among males, older people, and other individuals with low levels of education, low awareness of news, low income, and light use of social media. Moreover, public health authorities may consider cooperating with Twitter users having high levels of social influence to promote the acceptance of COVID-19 vaccines among all population groups.

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Section: Literature Reviewmentioning

confidence: 99%

The Evolution and Disparities of Online Attitudes Toward COVID-19 Vaccines: Year-long Longitudinal and Cross-sectional Study

Zhang¹,

Li²,

Liu³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Several studies used Twitter as the source of data for their analysis [24,29,30,35]. Social media data, particularly geotagged tweets, are valuable and cost-effective resources for near real-time spatial and spatiotemporal analyses.…”

Section: Discussionmentioning

confidence: 99%

“…However, the Voronoi map (Theisen polygon) was utilized in only one study [23]. The contribution of 11 articles to the spatial analysis of COVID-19 vaccination was only limited to mapping [24][25][26][27][28][29][30][31][32][33][34]. Although most mapping studies disregarded time components, and used static presentation of data, temporal variations or transmission dynamics were mapped in [24,29,30,[35][36][37].…”

Section: Vaccine Mappingmentioning

confidence: 99%

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Spatial Analysis of COVID-19 Vaccination: A Scoping Review

Mollalo

Mohammadi

Mavaddati

et al. 2021

IJERPH

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Spatial analysis of COVID-19 vaccination research is increasing in recent literature due to the availability of COVID-19 vaccination data that usually contain location components. However, to our knowledge, no previous study has provided a comprehensive review of this research area. Therefore, in this scoping review, we examined the breadth of spatial and spatiotemporal vaccination studies to summarize previous findings, highlight research gaps, and provide guidelines for future research. We performed this review according to the five-stage methodological framework developed by Arksey and O’Malley. We screened all articles published in PubMed/MEDLINE, Scopus, and Web of Science databases, as of 21 September 2021, that had employed at least one form of spatial analysis of COVID-19 vaccination. In total, 36 articles met the inclusion criteria and were organized into four main themes: disease surveillance (n = 35); risk analysis (n = 14); health access (n = 16); and community health profiling (n = 2). Our findings suggested that most studies utilized preliminary spatial analysis techniques, such as disease mapping, which might not lead to robust inferences. Moreover, few studies addressed data quality, modifiable areal unit problems, and spatial dependence, highlighting the need for more sophisticated spatial and spatiotemporal analysis techniques.

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“…Social media sentiment analysis allows agencies to examine public sentiment in a way that can help change public policy. For example, researchers analyzed Tweets about the COVID-19 vaccine to capture public perceptions so that future communications could be shaped to address prevalent concerns or misinformation [41]. In a study analyzing selected Twitter posts, Nemes and Kiss [42] found that sentiment toward COVID-19 was largely negative.…”

Section: Sentiment Analysis As a Measure Of People's Attitudesmentioning

confidence: 99%

COVID-19 and Tourism: Analyzing the Effects of COVID-19 Statistics and Media Coverage on Attitudes toward Tourism

et al. 2021

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COVID-19 has significantly influenced tourism, including tourists’ and residents’ attitudes toward tourism. At the same time, attitudes and consumer confidence are important for economic recovery in the tourism sector. This study explores the effects of the COVID-19 pandemic on people’s attitudes toward tourism by analyzing time-series data on the number of COVID-19 positive cases, vaccinations, news sentiment, a total number of daily mentions of tourism, and the share of voice for positive and negative sentiment toward tourism. The applied data analysis techniques include descriptive analysis, visual representation of data, data decomposition into trend and cycle components, unit root tests, Granger causality test, and multiple time series regression. The results demonstrate that the COVID-19 statistics and media coverage have significant effects on interest in tourism in general, as well as the positive and negative sentiment toward tourism. The results contribute to knowledge and practice by describing the effects of the disease statistics on attitudes toward tourism, introducing social media sentiment analysis as an opportunity to measure positive and negative sentiment toward tourism, and providing recommendations for government authorities, destination management organizations, and tourism providers.

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Public attitudes toward COVID-19 vaccines on English-language Twitter: A sentiment analysis

Cited by 111 publications

References 29 publications

The Evolution and Disparities of Online Attitudes Toward COVID-19 Vaccines: Year-long Longitudinal and Cross-sectional Study

The Evolution and Disparities of Online Attitudes Toward COVID-19 Vaccines: Year-long Longitudinal and Cross-sectional Study

Spatial Analysis of COVID-19 Vaccination: A Scoping Review

COVID-19 and Tourism: Analyzing the Effects of COVID-19 Statistics and Media Coverage on Attitudes toward Tourism

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