AI-Driven Modeling of Health Narratives, Risk Perception, and Decision Making

I am seeking academic collaborators and potential MSCA Postdoctoral Fellowship hosts working in healthcare AI, computational behavioral science, NLP, multimodal machine learning, and interpretable AI.

My proposed research focuses on developing theory informed and application driven AI methods for modeling health narratives and their influence on risk perception and decision-making using population-scale textual data. The project integrates behavioral science frameworks (risk perception, framing, heuristics) with large language models, transformer-based NLP, temporal analysis, and explainable AI approaches.

The research aims to develop scalable pipelines for extracting narratives, sentiments, discourse structures, and behavioral signals from large-scale multilingual data sources such as social media, online forums, and public health communication channels. The project also explores temporal narrative evolution, behavioral modeling, and multi-agent simulation approaches for understanding how health narratives influence public behavior and health-related decision-making.

Potential application domains include vaccination discourse, maternal and child health, mental health communication, chronic disease narratives, misinformation analysis, and AI-driven health communication strategies in low-resource settings.