PHORECAST: Enabling AI Understanding of Public Health Outreach Across Populations

2025-10-02

AI Summary

A critical gap exists in publicly available multimodal datasets supporting AI modeling of individual and collective responses to public health campaigns. To address this, we introduce PHORECAST—the first fine-grained, multimodal dataset designed specifically for health communication response forecasting. It captures both individual behavioral responses (e.g., clicks, shares, comments) and community-level engagement patterns (e.g., topic diffusion, sentiment clustering). Methodologically, PHORECAST uniquely enables heterogeneous response modeling across hierarchical levels (individual → community) and modalities (visual-textual content, textual posts, user demographic and behavioral profiles), integrating vision-language understanding, behavioral sequence prediction, and community dynamics analysis. The dataset comprises high-quality, rigorously annotated, and publicly accessible data, accompanied by standardized benchmark tasks. PHORECAST bridges a fundamental data void in socially aware health AI, substantially enhancing model capabilities in understanding complex public health discourse, improving predictive accuracy, and enabling scalable, personalized interventions.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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 individuallevel 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

Problem

• Predict individual behavioral responses to health messaging
• Forecast community-wide engagement patterns in public health
• Enable socially aware AI models for health communication

Innovation

• Multimodal dataset for health response prediction
• Enables individual and community behavior forecasting
• Supports socially aware AI model development