Depression is a common disease worldwide. It is difficult to diagnose and continues to be underdiagnosed. Because depressed patients constantly share their symptoms, major life events, and treatments on social media, researchers are turning to user-generated digital traces on social media for depression detection. Such methods have distinct advantages in combating depression because they can facilitate innovative approaches to fight depression and alleviate its social and economic burden. However, most existing studies lack effective means to incorporate established medical domain knowledge in depression detection or suffer from feature extraction difficulties that impede greater performance. Following the design science research paradigm, we propose a Deep Knowledge-aware Depression Detection (DKDD) framework to accurately detect social media users at risk of depression and explain the critical factors that contribute to such detection. Extensive empirical studies with real-world data demonstrate that, by incorporating domain knowledge, our method outperforms existing state-of-the-art methods. Our work has significant implications for IS research in knowledge-aware machine learning, digital traces utilization, and NLP research in IS. Practically, by providing early detection and explaining the critical factors, DKDD can supplement clinical depression screening and enable large-scale evaluations of a population's mental health status.

翻译：抑郁症是一种全球性的常见疾病，诊断困难且始终存在漏诊问题。由于抑郁症患者常在社交媒体上分享其症状、重大生活事件及治疗方式，研究者们开始利用用户在社交媒体上产生的数字足迹进行抑郁症检测。这类方法在对抗抑郁症方面具有显著优势，能够促进创新性防治手段的开发，并减轻其带来的社会经济负担。然而，现有研究大多缺乏有效整合已有医学领域知识的能力，或在特征提取方面存在困难，制约了检测性能的提升。遵循设计科学研究范式，我们提出了一种深度知识增强型抑郁症检测（DKDD）框架，旨在准确识别具有抑郁风险的社交媒体用户，并解释促成检测的关键因素。基于真实数据的广泛实证研究表明，通过融入领域知识，本方法在性能上超越了现有最优方法。本研究对信息系统领域中的知识增强机器学习、数字足迹利用以及自然语言处理研究具有重要启示。在实践中，DKDD通过提供早期检测并解释关键因素，可辅助临床抑郁症筛查，并实现大规模人群心理健康状况评估。