Public Reaction

Research on public reaction to health crises and scientific information focuses on understanding how individuals and communities respond to events like pandemics and new research findings, primarily using social media data as a proxy. Current studies employ various methods, including topic modeling, regression analysis, neural networks (like LSTMs and multilayer perceptrons), and generative models (like GPT-2) to analyze sentiment, identify key themes in public discourse, and predict responses to messaging. This work is significant for improving public health communication strategies, informing policy decisions, and providing scientists with insights into the societal impact of their research.