Teaching AI to Decode Vaccine Hesitancy Narratives: A Few-Shot Learning and Topic Modeling Approach

Vaccine hesitancy—which can be defined as a delay in acceptance or the refusal to get vaccinated—has substantially increased over the past decade. This study introduces a computational and qualitative approach designed to efficiently classify stance and uncover narratives in social media discourse without relying on extensive manual annotation. Using 298,356 COVID-19 vaccine-related X posts geolocated to South Carolina (June 2021–May 2022), zero-shot and few-shot learning with instruction-tuned large language models (Mistral-7B, Meta-Llama-3.1, and DeepSeek-7B) was applied for stance detection while Latent Dirichlet Allocation (LDA) was used for topic modeling. The topic modeling identified five dominant themes in vaccine hesitant conversations: skepticism of vaccine efficacy, comparative framing, scientific justification, disapproval of regulations, and distrust. Temporal analysis revealed that skepticism peaked during late 2021, coinciding with booster campaigns and mandate debates. These findings suggest that vaccine hesitancy is influenced through complex rhetorical strategies rather than misinformation alone. These underlying narratives often frame skepticism as rational and evidence-based, using scientific language and statistical reasoning to challenge the effectiveness of vaccines.