Palaniappan, M. (2022). Analyzing Twitter data to understand COVID-19 vaccine hesitancy following the federal private sector mandate. The Young Researcher, 6(1), 106-119. http://www.theyoungresearcher.com/papers/palaniappan.pdf
Since the rollout of the COVID-19 vaccine, vaccine hesitancy has been prevalent in the US, par- ticularly on social media such as Twitter. Significant efforts to understand the reasons behind anti- vaccine sentiments have been concentrated in the earlier stages of vaccine distribution and research suggests personal freedom to be a primary driver of vaccine hesitancy. This study aims to analyze anti-vaccine sentiment on Twitter following the first federal private sector mandate in November of 2021 to better understand how mandates impact vaccine sentiment in the later stages of the pandemic. Two machine learning models, roBERTa and LDA, were used to analyze a pre-curated Kaggle dataset of COVID-19 vaccine-related tweets. Results suggest that there is an overwhelmingly negative sentiment towards the vaccine. Reasons include efficacy and side-effect concerns, mistrust in institutions, and anti-mandate sentiments. This study identified common reasons behind vaccine hesitancy which future research can hopefully use to create targeted outreach programs.
Keywords: COVID-19, vaccine sentiment, vaccine hesitancy, machine learning
ISSN 2560-9815 (Print)
ISSN 2560-9823 (Online)
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