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The COVID-19 pandemic has presented significant challenges to the healthcareindustry and society as a whole. With the rapid development of COVID-19vaccines, social media platforms have become a popular medium for discussionson vaccine-related topics. Identifying vaccine-related tweets and analyzingthem can provide valuable insights for public health research-ers andpolicymakers. However, manual annotation of a large number of tweets istime-consuming and expensive. In this study, we evaluate the usage of LargeLanguage Models, in this case GPT-4 (March 23 version), and weak supervision,to identify COVID-19 vaccine-related tweets, with the purpose of comparingperformance against human annotators. We leveraged a manu-ally curatedgold-standard dataset and used GPT-4 to provide labels without any additionalfine-tuning or instructing, in a single-shot mode (no additional prompting).

Leveraging Large Language Models and Weak Supervision for Social Media  data annotation: an evaluation using COVID-19 self-reported vaccination  tweets