Hierarchical Multi-Label Classification of Online Vaccine Concerns

Understand

Vaccine concerns are an ever-evolving target, and can shift quickly as seen during the COVID-19 pandemic.

• Identifying longitudinal trends in vaccine concerns and misinformation might inform the healthcare space by helping public health efforts strategically allocate resources or information campaigns.
• We explore the task of detecting vaccine concerns in online discourse using large language models (LLMs) in a zero-shot setting without the need for expensive training datasets.
• Since real-time monitoring of online sources requires large-scale inference, we explore cost-accuracy trade-offs of different prompting strategies and offer concrete takeaways that may inform choices in system designs for current applications.