Articlebronze2022

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

Anasse Bari · Ryan J Cohen · Robin Zhao · Shailesh Apas Vasandani · Aashish Khubchandani · Madeline A. DiLorenzo +1 more

26 citations0/0 files storedClinical Infectious Diseases

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Vaccine Coverage and Hesitancy Misinformation and Its Impacts Sentiment Analysis and Opinion Mining

Abstract

BACKGROUND: Vaccination can help control the coronavirus disease 2019 (COVID-19) pandemic but is undermined by vaccine hesitancy. Social media disseminates information and misinformation regarding vaccination. Tracking and analyzing social media vaccine sentiment could better prepare health professionals for vaccination conversations and campaigns. METHODS: A real-time big data analytics framework was developed using natural language processing sentiment analysis, a form of artificial intelligence. The framework ingests, processes, and analyzes tweets for sentiment and content themes, such as natural health or personal freedom, in real time. A later dataset evaluated the relationship between Twitter sentiment scores and vaccination rates in the United States. RESULTS: The real-time analytics framework showed a widening gap in sentiment with more negative sentiment after vaccine rollout. After rollout, using a static dataset, an increase in positive sentiment was followed by an increase in vaccination. Lag cross-correlation analysis across US regions showed evidence that once all adults were eligible for vaccination, the sentiment score consistently correlated with vaccination rate with a lag of around 1 week. The Granger causality test further demonstrated that tweet sentiment scores may help predict vaccination rates. CONCLUSIONS: Social media has influenced the COVID-19 response through valuable information and misinformation and distrust. This tool was used to collect and analyze tweets at scale in real time to study sentiment and key terms of interest. Separate tweet analysis showed that vaccination rates tracked regionally with Twitter vaccine sentiment and might forecast changes in vaccine uptake and/or guide targeted social media and vaccination strategies. Further work is needed to analyze the interplay between specific populations, vaccine sentiment, and vaccination rates.

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References

2021Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA 2018Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate 2020Mapping global trends in vaccine confidence and investigating barriers to vaccine uptake: a large-scale retrospective temporal modelling study 2011Anti-vaccine activists, Web 2.0, and the postmodern paradigm – An overview of tactics and tropes used online by the anti-vaccination movement 2020COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data 2021Understanding COVID-19 vaccine hesitancy 2005Factors Associated With Refusal of Childhood Vaccines Among Parents of School-aged Children 2021Social Connectedness, Excessive Screen Time During COVID-19 and Mental Health: A Review of Current Evidence

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2023Examining the Negative Sentiments Related to Influenza Vaccination from 2017 to 2022: An Unsupervised Deep Learning Analysis of 261,613 Twitter Posts

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