Winds of Change: Impact of COVID-19 on Vaccine-related Opinions of Twitter users

TL;DR

This study analyzes Twitter discourse on COVID-19 vaccines, developing a high-accuracy classifier to track stance changes over time and identify factors influencing shifts in public opinion during the pandemic.

Contribution

It introduces a novel classifier for vaccine stance detection with 97% precision and investigates how Twitter users' vaccine opinions evolved from pre-COVID to COVID times.

Findings

Identified distinct topics discussed by vaccine supporters and opponents.

Detected significant shifts in user stances over time.

Provided insights into reasons behind stance changes.

Abstract

Administering COVID-19 vaccines at a societal scale has been deemed as the most appropriate way to defend against the COVID-19 pandemic. This global vaccination drive naturally fueled a possibility of Pro-Vaxxers and Anti-Vaxxers strongly expressing their supports and concerns regarding the vaccines on social media platforms. Understanding this online discourse is crucial for policy makers. This understanding is likely to impact the success of vaccination drives and might even impact the final outcome of our fight against the pandemic. The goal of this work is to improve this understanding using the lens of Twitter-discourse data. We first develop a classifier that categorizes users according to their vaccine-related stance with high precision (97%). Using this method we detect and investigate specific user-groups who posted about vaccines in pre-COVID and COVID times. Specifically, we identify distinct topics that these users talk about, and investigate how vaccine-related discourse has changed between pre-COVID times and COVID times. Finally, for the first time, we investigate the change of vaccine-related stances in Twitter users and shed light on potential reasons for such changes in stance. Our dataset and classifier are available at https://github.com/sohampoddar26/covid-vax-stance.