  This study explores the relationship between informational support seeking
questions, responses, and helpfulness ratings in online health communities. We
created a labeled data set of question-response pairs and developed multimodal
machine learning and deep learning models to reliably predict informational
support questions and responses. We employed explainable AI to reveal the
emotions embedded in informational support exchanges, demonstrating the
importance of emotion in providing informational support. This complex
interplay between emotional and informational support has not been previously
researched. The study refines social support theory and lays the groundwork for
the development of user decision aids. Further implications are discussed.
