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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)195-226
Journal / PublicationMIS Quarterly
Volume47
Issue number1
Online published27 Feb 2023
Publication statusPublished - Mar 2023

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 that are not intended to generate any emotional support (auxiliary content). Drawing from emotional contagion theories, we argue that even though emotional support may benefit targeted support seekers, it could have a negative impact on the emotions of other support seekers. 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 discuss options to alter the intervention volume, length, and frequency to tackle the challenge of the negative effect.

Research Area(s)

  • Emotional contagion, emotional support, text classification, deep learning, online health community

Citation Format(s)

Unintended Emotional Effects of Online Health Communities: A Text Mining-Supported Empirical Study. / Zhou, Jiaqi; Zhang, Qingpeng; Zhou, Sijia et al.
In: MIS Quarterly, Vol. 47, No. 1, 03.2023, p. 195-226.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review