  Social media posts provide valuable insight into the narrative of users and
their intentions, including providing an opportunity to automatically model
whether a social media user is depressed or not. The challenge lies in
faithfully modelling user narratives from their online social media posts,
which could potentially be useful in several different applications. We have
developed a novel and effective model called \texttt{NarrationDep}, which
focuses on detecting narratives associated with depression. By analyzing a
user's tweets, \texttt{NarrationDep} accurately identifies crucial narratives.
\texttt{NarrationDep} is a deep learning framework that jointly models
individual user tweet representations and clusters of users' tweets. As a
result, \texttt{NarrationDep} is characterized by a novel two-layer deep
learning model: the first layer models using social media text posts, and the
second layer learns semantic representations of tweets associated with a
cluster. To faithfully model these cluster representations, the second layer
incorporates a novel component that hierarchically learns from users' posts.
The results demonstrate that our framework outperforms other comparative models
including recently developed models on a variety of datasets.
