Generative AI for vaccine misbelief correction: Insights from targeting extraversion and pseudoscientific beliefs

Abstract

Background

Misinformation about vaccines is a significant barrier to public health, fueling hesitancy and resistance. Generative AI offers a scalable tool for assisting public health communicators in crafting targeted correction messages tailored to audience characteristics. This study investigates the effectiveness of AI-generated messages targeting extraversion and pseudoscientific beliefs compared to high-quality generic and non-vaccine-related messages.

Method

In a between-subjects experiment, 1435 U.S. adults were randomly assigned to one of four conditions: control, generic correction, extraversion-targeting correction, or pseudoscientific-belief-targeting correction. Participants rated their agreement with vaccine misbelief statements before and after exposure to a correction message. AI was used to generate the targeted correction messages, while the generic and control messages were sourced from real-world examples.

Results

Extraversion-targeting messages significantly reduced vaccine misbeliefs, performing comparably to high-quality generic messages, particularly among participants with higher extraversion levels. However, these effects did not extend to general vaccination attitudes. Pseudoscientific-belief-targeting messages were ineffective and, in some cases, backfired, reinforcing negative attitudes among individuals with strong pseudoscientific beliefs.

Conclusion

This study demonstrates the potential of AI-assisted message generation for crafting effective correction messages, particularly when targeting personality traits like extraversion. However, the findings suggest that certain AI-generated messages may be less effective or even counterproductive when targeting entrenched beliefs, underscoring the need for human oversight in refining AI-generated messages. Future research should explore additional audience characteristics and optimize human-AI collaboration to enhance the effectiveness of AI-generated correction messages in public health communication.