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

Jiaqi Zhou1; Qingpeng Zhang1; Sijia Zhou2; Xin Li3; Xiaoquan (Michael) Zhang4

1 School of Data Science, City University of Hong Kong, Kowloon, Hong Kong, China. · 2 Department of Electronic Commerce, School of Economics and Management, Southeast University, Nanjing, China · 3 Department of Information Systems, College of Business, City University of Hong Kong, Kowloon, Hong Kong, China · 4 Department of Management Science and Engineering, School of Economics and Management, Tsinghua University, Shenzhen, and Department of Decision Sciences and Managerial Economics, Business School, Chinese University of Hong Kong, Kowloon, Hong Kong, China

MIS Quarterly 2023

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.

DOI
10.25300/misq/2022/17018
Volume
47 (1)
Pages
195-226
Language
en
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Sources
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