Analyzing COVID-19 Vaccination Discourse on Twitter/X in the United States and United Kingdom
Abstract:
This
study conducted a sentiment analysis of COVID-19 vaccine-related tweets posted
between December 2020 and November 2021, using data from Twitter/X to explore
public perception in the UK and the USA. The goal was to contribute to the
growing use of digital tools in understanding public attitudes during pandemics
and to provide actionable insights for policymakers and health professionals. A
total of 194,378 English tweets were initially collected using Twitter Apify
and pre-processed to remove punctuation, stop words, and emoticons, resulting
in 144,911 usable tweets. From these, 42,311 tweets (24%) were identified as
originating from the UK and USA based on location-specific keywords. Sentiment
analysis was performed using the VADER lexicon-based tool, categorizing tweets
on a five-point scale ranging from strongly positive to strongly negative.
Statistical analyses, including Student’s t-test, Z-test, Pearson’s
correlation, and the Mann-Kendall test, were used to compare sentiment
patterns. Topic modeling using Latent Dirichlet Allocation (LDA) was also
employed to identify recurring themes. The findings revealed notable
differences in sentiment between the two countries, with statistically
significant variation in all sentiment categories except for the ‘positive’
category. The study offers meaningful insights into public sentiment dynamics
and underscores the potential for data-driven communication strategies in
public health policymaking.
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