The COVID-19 pandemic has presented significant challenges to the healthcare industry and society as a whole. With the rapid development of COVID-19 vaccines, social media platforms have become a popular medium for discussions on vaccine-related topics. Identifying vaccine-related tweets and analyzing them can provide valuable insights for public health research-ers and policymakers. However, manual annotation of a large number of tweets is time-consuming and expensive. In this study, we evaluate the usage of Large Language Models, in this case GPT-4 (March 23 version), and weak supervision, to identify COVID-19 vaccine-related tweets, with the purpose of comparing performance against human annotators. We leveraged a manu-ally curated gold-standard dataset and used GPT-4 to provide labels without any additional fine-tuning or instructing, in a single-shot mode (no additional prompting).

翻译：新冠疫情给医疗行业和整个社会带来了重大挑战。随着COVID-19疫苗的快速研发，社交媒体平台已成为讨论疫苗相关话题的流行媒介。识别并分析疫苗相关推文可为公共卫生研究人员和政策制定者提供宝贵见解。然而，人工标注大量推文既耗时又昂贵。本研究评估了大型语言模型（本文采用2023年3月版的GPT-4）与弱监督方法在识别COVID-19疫苗相关推文方面的应用，旨在将其性能与人类标注员进行比较。我们利用人工策化的黄金标准数据集，以单次模式（无额外提示）让GPT-4直接提供标签，无需进行任何微调或指令引导。