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Exploring Coronavirus Disease 2019 Vaccine Hesitancy on Twitter Using Sentiment Analysis and Natural Language

Anasse Bari1, Matthias Heymann1, Ryan J Cohen1

  • 1Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, New York, USA.

Clinical Infectious Diseases : an Official Publication of the Infectious Diseases Society of America
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Summary
This summary is machine-generated.

Analyzing social media vaccine sentiment reveals a correlation with vaccination rates. This data can help predict vaccine uptake and guide public health strategies for the coronavirus disease 2019 (COVID-19) pandemic.

Keywords:
COVID-19Twitterartificial intelligencevaccinationvaccine hesitancy

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Area of Science:

  • Public Health
  • Data Science
  • Computational Social Science

Background:

  • Vaccination is key to controlling the coronavirus disease 2019 (COVID-19) pandemic.
  • Vaccine hesitancy, fueled by social media misinformation, poses a significant challenge.
  • Monitoring social media sentiment can inform vaccination strategies.

Purpose of the Study:

  • To develop and evaluate a real-time big data analytics framework for tracking social media vaccine sentiment.
  • To assess the relationship between Twitter sentiment and COVID-19 vaccination rates in the United States.
  • To explore the predictive power of social media sentiment on vaccine uptake.

Main Methods:

  • Developed a real-time big data analytics framework using natural language processing and sentiment analysis.
  • Ingested, processed, and analyzed tweets for sentiment and content themes.
  • Utilized lag cross-correlation and Granger causality tests to evaluate sentiment-vaccination rate relationships.

Main Results:

  • Observed a widening gap with increased negative sentiment post-vaccine rollout.
  • Found that positive sentiment increases preceded vaccination rate increases.
  • Demonstrated a consistent correlation between Twitter sentiment scores and vaccination rates (lagging by ~1 week) and showed sentiment may predict uptake.

Conclusions:

  • Social media significantly influences public response to vaccination, disseminating both information and misinformation.
  • Real-time analysis of Twitter vaccine sentiment can track public opinion and potentially forecast vaccine uptake.
  • Findings suggest social media sentiment analysis can guide targeted vaccination campaigns and strategies.