Emotional and Informational Dynamics in Question-Response Pairs in Online Health Communities: A Multimodal Deep Learning Approach

Online health communities (OHCs) offer emotional and informational support to their users. However, past research has primarily treated these supports as separate, but they coexist in messages, making it essential to consider the emotional valence of text to understand the support being provided. This study examines how aligning questions and responses in OHCs reduces information gaps, and enhances support quality and perceived helpfulness. We use a labeled data set of question-response pairs to develop multimodal machine learning models to predict support interactions. Using explainable AI, we reveal the emotions within support exchanges, underscoring how emotional valence in the text determines informational support in OHCs and provide insight into the interaction between emotional and informational support. This study refines social support theory and establishes a foundation for decision aids and emotion-sensitive AI systems to deliver personalized social support tailored to users’ informational and emotional needs.