Unintended Emotional Effects of Online Health Communities: A Text Mining-Supported Empirical Study

Abstract

Online health communities (OHCs) play an important role in enabling patients to exchange information and obtain social support from each other. However, do OHC interactions always benefit patients? In this research, we investigate different mechanisms by which OHC content may affect patients’ emotions. Specifically, we notice users can read not only emotional support intended to help them but also emotional support targeting other persons or posts unintended to generate any emotional support (auxiliary content). Drawing from emotional contagion theories, we argue even though emotional support may benefit targeted support seekers, it could have a negative impact on the emotions of other patients. Our empirical study on an OHC for depression patients supports these arguments. Our findings are new to the literature and have critical practical implications since they suggest that we should carefully manage OHC-based interventions for depression patients to avoid unintended consequences. We design a novel deep learning model to differentiate emotional support from auxiliary content. Such differentiation is critical for identifying the negative effect of emotional support on unintended recipients. We also show the possibilities to alter the intervention volume, length, and frequency to tackle the challenge of the negative effect.

Keywords: emotional contagion, emotional support; text classification; deep learning; online health community