Working with Large Language Models to Enhance Messaging Effectiveness for Vaccine Confidence

Lucinda Gullison; Feng Fu

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arXiv · 2504.09857

Working with Large Language Models to Enhance Messaging Effectiveness for Vaccine Confidence

Lucinda Gullison·Feng Fu

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Abstract

Vaccine hesitancy and misinformation are significant barriers to achieving widespread vaccination coverage. Smaller public health departments may lack the expertise or resources to craft effective vaccine messaging. This paper explores the potential of ChatGPT-augmented messaging to promote confidence in vaccination uptake. We conducted a survey in which participants chose between pairs of vaccination messages and assessed which was more persuasive and to what extent. In each pair, one message was the original, and the other was augmented by ChatGPT. At the end of the survey, participants were informed that half of the messages had been generated by ChatGPT. They were then asked to provide both quantitative and qualitative responses regarding how knowledge of a message's ChatGPT origin affected their impressions. Overall, ChatGPT-augmented messages were rated slightly higher than the original messages. These messages generally scored better when they were longer. Respondents did not express major concerns about ChatGPT-generated content, nor was there a significant relationship between participants' views on ChatGPT and their message ratings. Notably, there was a correlation between whether a message appeared first or second in a pair and its score. These results point to the potential of ChatGPT to enhance vaccine messaging, suggesting a promising direction for future research on human-AI collaboration in public health communication.

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Lucinda Gullison, Feng Fu. 2025-04-14. Working with Large Language Models to Enhance Messaging Effectiveness for Vaccine Confidence. https://arxiv.org/abs/2504.09857

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