Should we tweet this? Generative response modeling for predicting reception of public health messaging on Twitter
This work addresses the problem for public health organizations like CDC or WHO in improving message effectiveness during crises, though it is incremental as it applies an existing generative model to a new domain.
The paper tackled predicting public reception of health messaging on Twitter by collecting COVID-19 and vaccine-related datasets and using a GPT-2 generative model to forecast responses, demonstrating its utility for optimizing message reception with statistical validation of semantic and sentiment accuracy.
The way people respond to messaging from public health organizations on social media can provide insight into public perceptions on critical health issues, especially during a global crisis such as COVID-19. It could be valuable for high-impact organizations such as the US Centers for Disease Control and Prevention (CDC) or the World Health Organization (WHO) to understand how these perceptions impact reception of messaging on health policy recommendations. We collect two datasets of public health messages and their responses from Twitter relating to COVID-19 and Vaccines, and introduce a predictive method which can be used to explore the potential reception of such messages. Specifically, we harness a generative model (GPT-2) to directly predict probable future responses and demonstrate how it can be used to optimize expected reception of important health guidance. Finally, we introduce a novel evaluation scheme with extensive statistical testing which allows us to conclude that our models capture the semantics and sentiment found in actual public health responses.