Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic.

ABSTRACT: Anti-vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due to anti-vaccination content widely available on social media, including Twitter. Being able to identify anti-vaccination tweets could provide useful information for formulating strategies to reduce anti-vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti-vaccination tweets that were published during the COVID-19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long short-term memory networks with pre-trained GLo

SUBMITTER: To QG 

PROVIDER: S-EPMC8069687 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature