ArticlePreventive medicine reports2024
Accuracy of a large language model in distinguishing anti- and pro-vaccination messages on social media: The case of human papillomavirus vaccination.
Article in Preventive medicine reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Beyond Accuracy: A Mixed-Methods Audit of Chain-of-Thought Failures in LLM-Based COVID-19 Vaccine Stance Detection.Journal of medical systems · 2026Article
- Efficient information extraction using LLMs and knowledge distillation: A study on HPV health communication.PLOS digital health · 2026Article
- A suite of large language models for public health infoveillance.NPJ digital medicine · 2026Article
- Review
- Cyberbullying victimization identification and large language model-assisted assessment: a study of cyberbullying victimization lexicon construction and validation.Frontiers in psychiatry · 2026Article
- PH-LLM: Public Health Large Language Models for Infoveillance.medRxiv : the preprint server for health sciences · 2025Article
- Proficiency, Clarity, and Objectivity of Large Language Models Versus Specialists' Knowledge on COVID-19's Impacts in Pregnancy: Cross-Sectional Pilot Study.JMIR formative research · 2025Article
- Sentiment analysis of research attention: the Altmetric proof of concept.Frontiers in research metrics and analytics · 2025Article
- Use of ChatGPT to Explore Gender and Geographic Disparities in Scientific Peer Review.Journal of medical Internet research · 2024Article
- Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions.BMC medicine · 2024Review
- A comparison of the persuasiveness of human and ChatGPT generated pro-vaccine messages for HPV.Frontiers in public health · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 ( 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.
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