PHORECAST: Enabling AI Understanding of Public Health Outreach Across Populations

Rifaa Qadri, Anh N Nhu, Swati Ramnath, Laura Yu Zheng, Raj Bhansali, Sylvette La Touche-Howard, Tracy M. Zeeger, Tom Goldstein, Ming Lin

Keywords: predictive models, vision language models

TL;DR: We analyze public health opinion based on personal/external factors and formation processes. Our multi-modal dataset links human profiles to their multimedia campaign interactions.

Abstract: Understanding how diverse individuals and communities respond to persuasive messaging holds significant potential for advancing personalized and socially aware machine learning. While Large Vision and Language Models (VLMs) offer promise, their ability to emulate nuanced, heterogeneous human responses, particularly in high stakes domains like public health, remains underexplored due in part to the lack of comprehensive, multimodal dataset. We introduce PHORECAST - Public Health Outreach REceptivity and CAmpaign Signal Tracking), a multimodal dataset curated to enable fine-grained prediction of both individual-level behavioral responses and community-wide engagement patterns to health messaging. This dataset supports tasks in multimodal understanding, response prediction, personalization, and social forecasting, allowing rigorous evaluation of how well modern AI systems can emulate, interpret, and anticipate heterogeneous public sentiment and behavior. By providing a new dataset to enable AI advances for public health, PHORECAST aims to catalyze the development of models that are not only more socially aware but also aligned with the goals of adaptive and inclusive health communication.

Primary Area: datasets and benchmarks

Submission Number: 13628