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Title:
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USE OF TWO TOPIC MODELING METHODS
TO INVESTIGATE COVID VACCINE HESITANCY |
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Author(s):
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Phillip Ma, Qing Zeng-Treitler and Stuart J. Nelson |
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ISBN:
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978-989-8704-30-6 |
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Editors:
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Piet Kommers and Mário Macedo |
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Year:
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2021 |
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Edition:
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Single |
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Keywords:
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Vaccine Hesitancy, Topic Modeling, Twitter |
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Type:
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Short |
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First Page:
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221 |
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Last Page:
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226 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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COVID vaccine hesitancy in the face of a pandemic is a concern for public health researchers and policy makers who aim
to achieve herd immunity. We investigated the COVID vaccine hesitancy by analyzing Twitter posts (tweets) using two
topic modeling methods: Latent Dirichlet Allocation (LDA) and Top2Vec. Of the two methods, Top2Vec was able to reveal
topics which directly discussed Vaccine Hesitancy and thus offered more utility for this research topic. Common reasons
for vaccine hesitancy found in the dataset included concerns about recent (at the time of tweet collection) news regarding
side effects associated with the COVID vaccines, and a mixture of scientific and government skepticism related to vaccine
development and distribution. |
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