Revealing public opinion towards COVID-19 vaccines with Twitter Data in the United States: a spatiotemporal perspective

Abstract

Background: The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States (US) and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance. Objective: The aim of this study is to investigate public opinion and perception on COVID-19 vaccines by investigating the spatiotemporal trends of their sentiment and emotion towards vaccines, as well as how such trends relate to popular topics on Twitter in the US. Methods: We collected over 300,000 geotagged tweets in the US from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified three phases along the pandemic timeline with the significant changes of public sentiment and emotion. We further linked the changes to eleven key events and major topics as the potential drivers to induce such changes via cloud mapping of keywords and topic modeling. Results: An increasing trend of positive sentiment in parallel with the decrease of negative sentiment are generally observed in most states, reflecting the rising confidence and anticipation of the public towards COVID-19 vaccines. The overall tendency of the eight types of emotion implies the trustiness and anticipation of the public to vaccination, accompanied by the mixture of fear, sadness and anger. Critical social/international events and/or the announcements of political leaders and authorities may have potential impacts on the public opinion on vaccines. These factors, along with important topics and manual reading of popular posts on eleven key events, help identify underlying themes and validate insights from the analysis. Conclusions: The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics and promote the confidence of individuals within a certain region or community, towards vaccines.

Sentiment and emotion trends in nurses' tweets about the COVID‐19 pandemic

“…The drop-in sentiment score was also reported by Hu et al (2021) and was attributed to the misinformation and conspiracy theories related to the COVID-19 pandemic and vaccines. Our investigation also showed a gradual increase in the sentiments score by the mid of September when the vaccine distribution plan was released and when the Pfizer vaccine was reported to be 95% effective.…”

Spatial Analysis of COVID-19 Vaccination: A Scoping Review

“…Moreover, changes in emotions were most notable among less populated states in the central US. Similarly, in this country, Hu et al [29] used choropleth maps, and observed an increasing trend in positive sentiments and a decrease in negative public opinions towards vaccines in most states, reflecting the rising confidence and anticipation of the public towards vaccines.…”

“…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.…”

“…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].…”

“…Root-Bernstein [57] D, R 3 Whitehead, Scott [49] D, R 3 Whitehead, Carr [50] D, R 3 Bauer, Zhang [38] D, R 3 Chakraborty, Sharma [26] D, R 3 Qunaibi, Basheti [33] D, R 3 Lei [58] D 1 Zhou, Zhou [55] D, R, N, M 7 Ali, Rahman [24] D, R 3 Hu, Wang [29] D 1 Grauer, Löwen [28] N, M 4 Krzysztofowicz and Osi ńska-Skotak [23] D, N, M 5…”