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At the peak of the COVID-19 pandemic, numerous countries worldwide sought tomobilize vaccination campaigns in an attempt to curb the spread and number ofdeaths caused by the virus. One avenue in which information regarding COVIDvaccinations is propagated is that of scientific articles, which provide acertain level of credibility regarding this. Hence, this increases theprobability that people who view these articles would get vaccinated if thearticles convey a positive message on vaccinations and conversely decreases theprobability of vaccinations if the articles convey a negative message. Thisbeing said, this study aims to investigate the correlation between articlesentiments and the corresponding increase or decrease in vaccinations in theUnited States. To do this, a lexicon-based sentiment analysis was performed intwo steps: first, article content was scraped via a Python library calledBeautifulSoup, and second, VADER was used to obtain the sentiment analysisscores for each article based on the scraped text content. Results suggest thatthere was a relatively weak correlation between the average sentiment score ofarticles and the corresponding increase or decrease in COVID vaccination ratesin the US.

Analyzing the Impact of Sentiments of Scientific Articles on COVID-19 Vaccination Rates