Depression is a leading mental health problem affecting 300 million people globally. Recent studies show that social networks provide a tremendous potential for mental health professionals as a source of supplemental information about their patients. This study presents a methodological framework for clinical decision support systems (CDSSs) through analysis of social network data to distinguish the language usage of individuals with early signs of depression (i.e., contrast language analysis). By analyzing the contrast language patterns of different user groups, we are able to uncover constructive and actionable insights into the pain points and characteristics of users with signs of depression as decision support mechanisms for clinicians during intervention, (early) diagnosis and treatment plans. First, we discover terms that represent contrasting language by analyzing the percentage difference of terms in two user groups, labeled as”depressed” and”non-depressed” for ease of reference. Second, by building topic models based on social network contents, the topic-level contrast features are discovered. Finally, we consider the structure of the social network to discover the network-level contrast features. To illustrate the effectiveness of the proposed framework, we present a case study on early depression detection using a real-world dataset. The proposed framework has methodological contributions in enhancing the features and functionalities of CDSS for clinicians. It also contributes to evidence-based health research through cost-effective data and analytical insights that can supplement or improve the traditional survey and time-consuming interview methods.

抑郁症是影响全球 3 亿人的主要心理健康问题。最近的研究表明，社交网络为心理健康专业人员提供了巨大的潜力，可以作为他们患者的补充信息来源。本研究通过对社交网络数据的分析，为临床决策支持系统 (CDSS) 提供了一个方法论框架，以区分具有抑郁症早期迹象的个体的语言使用情况（即对比语言分析）。通过分析不同用户群体的对比语言模式，我们能够发现对有抑郁症状的用户的痛点和特征的建设性和可操作的见解，作为临床医生在干预、（早期）诊断和治疗计划期间的决策支持机制。第一的，我们通过分析两个用户组中术语的百分比差异来发现代表对比语言的术语，标记为“抑郁”和“非抑郁”以便于参考。其次，通过建立基于社交网络内容的话题模型，发现主题级对比特征。最后，我们考虑社交网络的结构以发现网络级对比特征。为了说明所提出框架的有效性，我们提出了一个使用真实数据集进行早期抑郁检测的案例研究。所提出的框架在为临床医生增强 CDSS 的特性和功能方面具有方法学贡献。它还通过具有成本效益的数据和分析见解有助于基于证据的健康研究，这些数据和分析见解可以补充或改进传统的调查和耗时的访谈方法。