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.

中文翻译：

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在线健康社区的意外情绪影响：文本挖掘支持的实证研究

在线健康社区 (OHC) 在使患者能够交换信息和获得彼此的社会支持方面发挥着重要作用。然而，OHC 相互作用是否总能使患者受益？在这项研究中，我们调查了 OHC 含量可能影响患者情绪的不同机制。具体来说，我们注意到用户不仅可以阅读旨在帮助他们的情感支持，还可以阅读针对其他人的情感支持或无意产生任何情感支持的帖子（辅助内容）。根据情绪传染理论，我们认为，尽管情绪支持可能会使目标支持寻求者受益，但它可能对其他支持寻求者的情绪产生负面影响。我们对抑郁症患者的 OHC 的实证研究支持这些论点。我们的研究结果是文献中的新发现，并且具有重要的实际意义，因为它们表明我们应该谨慎管理针对抑郁症患者的基于 OHC 的干预措施，以避免意外后果。我们设计了一种新颖的深度学习模型来区分情感支持和辅助内容。这种区分对于识别情感支持对非预期接受者的负面影响至关重要。我们还讨论了改变干预量、长度和频率的选项，以应对负面影响的挑战。这种区分对于识别情感支持对非预期接受者的负面影响至关重要。我们还讨论了改变干预量、长度和频率的选项，以应对负面影响的挑战。这种区分对于识别情感支持对非预期接受者的负面影响至关重要。我们还讨论了改变干预量、长度和频率的选项，以应对负面影响的挑战。