Twitter Analysis of Vaccination in a Post COVID-19 Era for the Society for Risk Analysis Meeting 2021
The Vaccination in a Post COVID-19 Era companion website provides an overview of the conversation dynamics on Twitter about the vaccination in a post Covid-19 era. We use a Natural Language Processing (NLP) approach including a Structural Topic Modeling (STM) approach to analyze the vaccination-related tweets. The tweets are analyzed by periods of anomalies.
We collected tweets and their metadata (date, username, retweets count, hashtags, etc.) over three years using a framing strategy to assemble our datasets.
The framing strategy: “vaccine” OR “vaccines” OR “vaccinate” OR “vaccination” OR “vaccineswork” OR “antivax” OR “vaccinesdontwork” OR “provax” OR “vaxwithme” OR “antivaxxers” OR “immunization.”
The data is gathered using an R-based program that interrogated the streaming Application Programming Interface (API) of Twitter. The dataset contains 2,601,702 messages from January 01, 2018 to April 21, 2021.
For attribution, please cite this work as
Marcellis-Warin & Warin (2021, Dec. 8). Thierry Warin, PhD: [Conference] Twitter Analysis of Vaccination in a Post COVID-19 Era. Retrieved from https://warin.ca/posts/article-sra-vaccination/
BibTeX citation
@misc{marcellis-warin2021[conference],
author = {Marcellis-Warin, Nathalie de and Warin, Thierry},
title = {Thierry Warin, PhD: [Conference] Twitter Analysis of Vaccination in a Post COVID-19 Era},
url = {https://warin.ca/posts/article-sra-vaccination/},
year = {2021}
}