TL;DR
This study analyzes over 16 million tweets about COVID-19 vaccines to understand negative sentiments, their topics, and how public discourse evolved, providing insights for policymakers and health authorities.
Contribution
It introduces a large-scale analysis of vaccine-related negative tweets using machine learning and topic modeling, revealing key concerns and their temporal dynamics.
Findings
Negativity towards vaccines decreased over time.
Identified 37 key discussion topics including conspiracies and safety concerns.
Conspiracy theories like 5G and microchips are prevalent in negative discourse.
Abstract
Since the onset of the COVID-19 pandemic, vaccines have been an important topic in public discourse. The discussions around vaccines are polarized as some see them as an important measure to end the pandemic, and others are hesitant or find them harmful. This study investigates posts related to COVID-19 vaccines on Twitter and focuses on those which have a negative stance toward vaccines. A dataset of 16,713,238 English tweets related to COVID-19 vaccines was collected covering the period from March 1, 2020, to July 31, 2021. We used the Scikit-learn Python library to apply a support vector machine (SVM) classifier to identify the tweets with a negative stance toward the COVID-19 vaccines. A total of 5,163 tweets were used to train the classifier, out of which a subset of 2,484 tweets were manually annotated by us and made publicly available. We used the BERTtopic model to extract and…
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