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：抑郁症是一种全球常见的疾病，诊断困难且长期存在漏诊问题。由于抑郁症患者常在社交媒体上持续分享其症状、重大生活事件及治疗经历，研究人员开始利用用户在社交媒体上生成的数字痕迹进行抑郁症检测。此类方法在对抗抑郁症方面具有显著优势，因其能推动创新手段以减轻疾病带来的社会与经济负担。然而，现有研究大多缺乏有效整合医学领域知识的手段，或面临特征提取困难导致性能受限的问题。遵循设计科学研究范式，我们提出了一种深度知识感知抑郁症检测（DKDD）框架，旨在精准识别具有抑郁风险的社交媒体用户，并解释影响检测结果的关键因素。基于真实世界数据的广泛实证研究表明，通过融合领域知识，我们的方法优于现有最先进的方法。本研究对信息系统领域中的知识感知机器学习、数字痕迹利用以及自然语言处理研究具有重要启示。在实践中，通过早期检测与关键因素解释，DKDD能够补充临床抑郁症筛查，并实现大规模人群心理健康状况评估。