  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.
