The Role of Emotions in Informational Support Question-Response Pairs in Online Health Communities: A Multimodal Deep Learning Approach

TL;DR

This paper investigates how emotions influence informational support in online health communities using multimodal deep learning, revealing emotional cues' importance in support exchanges and aiding future user decision tools.

Contribution

It introduces a novel multimodal deep learning approach to analyze emotions in informational support exchanges, filling a research gap in online health community interactions.

Findings

Emotion plays a crucial role in informational support exchanges.

Explainable AI reveals emotional cues embedded in support interactions.

The study advances understanding of emotional influence in online health support.

Abstract

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.