ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection

Objectives: COVID-19 (SARS-CoV-2) pandemic has infected hundreds of millions and inflicted millions of deaths around the globe. Fortunately, the introduction of COVID-19 vaccines provided a glimmer of hope and a pathway to recovery. However, owing to misinformation being spread on social media and other platforms, there has been a rise in vaccine hesitancy which can lead to a negative impact on vaccine uptake in the population. The goal of this research is to introduce a novel machine learning-based COVID-19 vaccine misinformation detection framework. Study design: We collected and annotated COVID-19 vaccine tweets and trained machine learning algorithms to classify vaccine misinformation. Methods: More than 15,000 tweets were annotated as misinformation or general vaccine tweets using reliable sources and validated by medical experts. The classification models explored were XGBoost, LSTM, and BERT transformer model. Results: The best classification performance was obtained using BERT, resulting in 0.98 F1-score on the test set. The precision and recall scores were 0.97 and 0.98, respectively. Conclusion: Machine learning-based models are effective in detecting misinformation regarding COVID19 vaccines on social media platforms. (c) 2021 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Automated summary

This research introduces a novel machine learning-based framework for detecting COVID-19 vaccine misinformation. By collecting and annotating vaccine tweets and training machine learning algorithms, the study achieves good classification performance using the BERT model.