Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective

Abstract

Background The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States 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 was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter. Methods We collected over 300,000 geotagged tweets in the United States 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 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes. Results An increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors 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 that individuals within a certain region or community have towards vaccines.

The times, they are a-changin’: tracking shifts in mental health signals from early phase to later phase of the COVID-19 pandemic in Australia

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“…The public’s negative sentiment (eg, depression, fear, sadness and anxiety) toward COVID-19 has been observed in studies in the USA,2 UK,3 Australia4 and China,5 alongside a number of European nations 6. Furthermore, the elevated need for mental health services has been reported, although the increasing prevalence of vaccination may act to lower the negativity toward COVID-19 7. As the United Nations’ policy brief COVID-19 and the need for action on mental health,8 it concluded that an increased level of mental health crises in the era of COVID-19 is a priority worth a prompt response urgently planned by each country.…”

Investigating the Public Sentiment in Major Public Emergencies Through the Complex Networks Method: A Case Study of COVID-19 Epidemic

“…The big data method has created new stimulation for the research of public sentiment ( 7 – 10 ). Hu et al ( 11 ) accrued over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021 and examined the spatiotemporal patterns of public sentiment and emotion over time at the national and state levels. The researchers discovered that in most states, an increasing trend in positive sentiment was observed in conjunction with a decrease in negative sentiment, reflecting the public's rising confidence and anticipation toward vaccines.…”

Comparison of the Erectile Dysfunction Drugs Sildenafil and Tadalafil Using Patient Medication Reviews: Topic Modeling Study

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