Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis

Roe, Charlotte, Lowe, Madison, Williams, Benjamin and Miller, Clare (2021) Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis. International Journal of Environmental Research and Public Health, 18 (24). p. 13028. ISSN 1660-4601

Full content URL: https://doi.org/10.3390/ijerph182413028

Documents
Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis
Open Access manuscript
[img]
[Download]
[img]
Preview
PDF
ijerph-18-13028.pdf - Whole Document
Available under License Creative Commons Attribution 4.0 International.

3MB
Item Type:Article
Item Status:Live Archive

Abstract

Vaccine hesitancy is an ongoing concern, presenting a major threat to global health. SARS-CoV-2 COVID-19 vaccinations are no exception as misinformation began to circulate on social media early in their development. Twitter’s Application Programming Interface (API) for Python was used to collect 137,781 tweets between 1 July 2021 and 21 July 2021 using 43 search terms relating to COVID-19 vaccines. Tweets were analysed for sentiment using Microsoft Azure (a machine learning approach) and the VADER sentiment analysis model (a lexicon-based approach), where the Natural Language Processing Toolkit (NLTK) assessed whether tweets represented positive, negative or neutral opinions. The majority of tweets were found to be negative in sentiment (53,899), followed by positive (53,071) and neutral (30,811). The negative tweets displayed a higher intensity of sentiment than positive tweets. A questionnaire was distributed and analysis found that individuals with full vaccination histories were less concerned about receiving and were more likely to accept the vaccine. Overall, we determined that this sentiment-based approach is useful to establish levels of vaccine hesitancy in the general public and, alongside the questionnaire, suggests strategies to combat specific concerns and misinformation.

Keywords:SARS-CoV-2, COVID-19, vaccination, sentiment analysis, twitter, Python, VADER, NLTK
Subjects:C Biological Sciences > C540 Virology
G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G400 Computer Science
C Biological Sciences > C500 Microbiology
Divisions:College of Science > School of Life Sciences
ID Code:47588
Deposited On:21 Dec 2021 12:05

Repository Staff Only: item control page