COVID-19 Vaccine Misinformation in Middle Income Countries
Jongin Kim Byeo Rhee Bak Aditya Agrawal Jiaxi Wu Veronika J. Wirtz Traci Hong Derry Wijaya
This paper introduces a multilingual dataset of COVID-19 vaccine
misinformation, consisting of annotated tweets from three middle-income
countries: Brazil, Indonesia, and Nigeria. The expertly curated dataset
includes annotations for 5,952 tweets, assessing their relevance to COVID-19
vaccines, presence of misinformation, and the themes of the misinformation. To
address challenges posed by domain specificity, the low-resource setting, and
data imbalance, we adopt two approaches for developing COVID-19 vaccine
misinformation detection models: domain-specific pre-training and text
augmentation using a large language model. Our best misinformation detection
models demonstrate improvements ranging from 2.7 to 15.9 percentage points in
macro F1-score compared to the baseline models. Additionally, we apply our
misinformation detection models in a large-scale study of 19 million unlabeled
tweets from the three countries between 2020 and 2022, showcasing the practical
application of our dataset and models for detecting and analyzing vaccine
misinformation in multiple countries and languages. Our analysis indicates that
percentage changes in the number of new COVID-19 cases are positively
associated with COVID-19 vaccine misinformation rates in a staggered manner for
Brazil and Indonesia, and there are significant positive associations between
the misinformation rates across the three countries.