Bidirectional Encoder Representations from Transformers for the COVID-19 vaccine stance classification

ABSTRACT

Vaccine-related information is awash on social media platforms like Twitter and Facebook. One party supports vaccination, while the other opposes vaccination and promotes misconceptions and misleading information about the risks of vaccination. The analysis of social media posts can give significant information into public opinion on vaccines, which can help government authorities in decision-making. In this work, an ensemble-based BERT model has been proposed for the classification of COVID-19 vaccine-related tweets into AntiVax, ProVax, and neural sentiment classes. The proposed model performed significantly well with a micro F1-score of 0.532 and an accuracy of 0.532 and achieved the second rank in the shared competition. © 2021 Copyright for this paper by its authors.