  Automatic depression detection on Twitter can help individuals privately and
conveniently understand their mental health status in the early stages before
seeing mental health professionals. Most existing black-box-like deep learning
methods for depression detection largely focused on improving classification
performance. However, explaining model decisions is imperative in health
research because decision-making can often be high-stakes and life-and-death.
Reliable automatic diagnosis of mental health problems including depression
should be supported by credible explanations justifying models' predictions. In
this work, we propose a novel explainable model for depression detection on
Twitter. It comprises a novel encoder combining hierarchical attention
mechanisms and feed-forward neural networks. To support psycholinguistic
studies, our model leverages metaphorical concept mappings as input. Thus, it
not only detects depressed individuals, but also identifies features of such
users' tweets and associated metaphor concept mappings.
