构建多模态数据集，让AI读懂不同人群对健康宣传的反应。
PHORECAST: Enabling AI Understanding of Public Health Outreach Across Populations
- 基于多模态数据构建公众响应预测框架
- 支持个体行为与社区群体行为的精细预测
- 适合关注社会意识型AI与健康传播的研究者
理解不同个体和社区对说服性信息的反应，对推动个性化和社会敏感的机器学习具有重要意义。尽管大视觉语言模型（VLMs）展现出潜力，但其在高风险领域如公共卫生中模拟复杂多样的人类反应能力仍受限于缺乏全面的多模态数据集。我们提出PHORECAST（公共健康宣传感知与活动信号追踪），一个精心构建的多模态数据集，旨在实现对个体行为反应和社区层面参与模式的细粒度预测。该数据集支持多模态理解、响应预测、个性化推荐和社交预测任务，可严格评估现代AI系统在模拟、解释和预判异质公众情绪与行为方面的表现。通过提供促进公共卫生领域AI进步的新数据集，PHORECAST致力于推动更具备社会意识且契合动态包容性健康传播目标的模型发展。
原文摘要 · Abstract (English)
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
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