  According to the World Health Organization (WHO), one in four people will be
affected by mental disorders at some point in their lives. However, in many
parts of the world, patients do not actively seek professional diagnosis
because of stigma attached to mental illness, ignorance of mental health and
its associated symptoms. In this paper, we propose a model for passively
detecting mental disorders using conversations on Reddit. Specifically, we
focus on a subset of mental disorders that are characterized by distinct
emotional patterns (henceforth called emotional disorders): major depressive,
anxiety, and bipolar disorders. Through passive (i.e., unprompted) detection,
we can encourage patients to seek diagnosis and treatment for mental disorders.
Our proposed model is different from other work in this area in that our model
is based entirely on the emotional states, and the transition between these
states of users on Reddit, whereas prior work is typically based on
content-based representations (e.g., n-grams, language model embeddings, etc).
We show that content-based representation is affected by domain and topic bias
and thus does not generalize, while our model, on the other hand, suppresses
topic-specific information and thus generalizes well across different topics
and times. We conduct experiments on our model's ability to detect different
emotional disorders and on the generalizability of our model. Our experiments
show that while our model performs comparably to content-based models, such as
BERT, it generalizes much better across time and topic.
