Objective. Vaccination has engendered a spectrum of public opinions, with social media acting as a crucial platform for health-related discussions. The emergence of artificial intelligence technologies, such as large language models (LLMs), offers a novel opportunity to efficiently investigate public discourses. This research assesses the accuracy of ChatGPT, a widely used and freely available service built upon an LLM, for sentiment analysis to discern different stances toward Human Papillomavirus (HPV) vaccination. Methods. Messages related to HPV vaccination were collected from social media supporting different message formats: Facebook (long format) and Twitter (short format). A selection of 1,000 human-evaluated messages was input into the LLM, which generated multiple response instances containing its classification results. Accuracy was measured for each message as the level of concurrence between human and machine decisions, ranging between 0 and 1. Results. Average accuracy was notably high when 20 response instances were used to determine the machine decision of each message: .882 (SE = .021) and .750 (SE = .029) for anti- and pro-vaccination long-form; .773 (SE = .027) and .723 (SE = .029) for anti- and pro-vaccination short-form, respectively. Using only three or even one instance did not lead to a severe decrease in accuracy. However, for long-form messages, the language model exhibited significantly lower accuracy in categorizing pro-vaccination messages than anti-vaccination ones. Conclusions. ChatGPT shows potential in analyzing public opinions on HPV vaccination using social media content. However, understanding the characteristics and limitations of a language model within specific public health contexts remains imperative.

疫苗接种在社交媒体上引发了多种不同的公众意见，本研究评估了基于大型语言模型的ChatGPT用于情感分析，以识别人乳头瘤病毒（HPV）疫苗接种的不同立场，结果表明ChatGPT在分析社交媒体上的公众意见方面具有潜力，但需要在特定公共卫生背景下了解语言模型的特点和限制。

Accuracy of a Large Language Model in Distinguishing Anti- And
  Pro-vaccination Messages on Social Media: The Case of Human Papillomavirus
  Vaccination

大型语言模型在社交媒体上区分反疫苗和亲疫苗信息的准确性：以人类乳头瘤病毒疫苗为例