The Role of Emotions in Informational Support Question-Response Pairs in Online Health Communities: A Multimodal Deep Learning Approach
This research addresses the problem of understanding and improving support in online health communities for users, and it is incremental as it builds on social support theory with a novel multimodal approach.
The study tackled the relationship between informational support questions, responses, and helpfulness in online health communities by developing multimodal deep learning models to predict these elements and using explainable AI to reveal the role of emotions in informational support exchanges, showing that emotion is important in providing informational support.
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.