Li Min, Gu Dongxiao, Li Rui, Gu Yadi, Liu Hu, Su Kaixiang, Wang Xiaoyu, Zhang Gongrang
School of Management, Hefei University of Technology, Hefei, China.
Center for Mental Health Education, University of Shanghai for Science and Technology, Shanghai, China.
J Med Internet Res. 2025 Jan 14;27:e60292. doi: 10.2196/60292.
In online mental health communities, the interactions among members can significantly reduce their psychological distress and enhance their mental well-being. The overall quality of support from others varies due to differences in people's capacities to help others. This results in some support seekers' needs being met, while others remain unresolved.
This study aimed to examine which characteristics of the comments posted to provide support can make support seekers feel better (ie, result in cognitive change).
We used signaling theory to model the factors affecting cognitive change and used consulting strategies from the offline, face-to-face psychological counseling process to construct 6 characteristics: intimacy, emotional polarity, the use of first-person words, the use of future-tense words, specificity, and language style. Through text mining and natural language processing (NLP) technology, we identified linguistic features in online text and conducted an empirical analysis using 12,868 online mental health support reply data items from Zhihu to verify the effectiveness of those features.
The findings showed that support comments are more likely to alter support seekers' cognitive processes if those comments have lower intimacy (β=-1.706, P<.001), higher positive emotional polarity (β=.890, P<.001), lower specificity (β=-.018, P<.001), more first-person words (β=.120, P<.001), more future- and present-tense words (β=.301, P<.001), and fewer function words (β=-.838, P<.001). The result is consistent with psychotherapists' psychotherapeutic strategy in offline counseling scenarios.
Our research contributes to both theory and practice by proposing a model to reveal the factors that make support seekers feel better. The findings have significance for support providers. Additionally, our study offers pointers for managing and designing online communities for mental health.
在在线心理健康社区中,成员之间的互动可以显著减轻他们的心理困扰,提升他们的心理健康水平。由于人们帮助他人的能力存在差异,他人提供的支持的整体质量也各不相同。这导致一些寻求帮助者的需求得到满足,而另一些则仍未解决。
本研究旨在探讨提供支持的评论的哪些特征能够让寻求帮助者感觉更好(即导致认知改变)。
我们运用信号理论对影响认知改变的因素进行建模,并采用线下面对面心理咨询过程中的咨询策略构建了6个特征:亲密程度、情感极性、第一人称词汇的使用、将来时态词汇的使用、具体程度和语言风格。通过文本挖掘和自然语言处理(NLP)技术,我们识别了在线文本中的语言特征,并使用来自知乎的12868条在线心理健康支持回复数据项进行实证分析,以验证这些特征的有效性。
研究结果表明,如果支持性评论的亲密程度较低(β=-1.706,P<.001)、积极情感极性较高(β=.890,P<.001)、具体程度较低(β=-.018,P<.001)、第一人称词汇较多(β=.120,P<.001)、将来时态和现在时态词汇较多(β=.301,P<.001)且功能词较少(β=-.838,P<.001),那么这些评论更有可能改变寻求帮助者的认知过程。这一结果与心理治疗师在离线咨询场景中的心理治疗策略一致。
我们的研究通过提出一个模型来揭示让寻求帮助者感觉更好的因素,为理论和实践都做出了贡献。这些发现对支持提供者具有重要意义。此外,我们的研究为心理健康在线社区的管理和设计提供了指导。