Abstract:
Depression has become a big problem in our
society today. It is also a major reason for suicide, especially
among teenagers. In the current outbreak of coronavirus disease
(COVID-19), the affected countries have recommended social
distancing and lockdown measures. Resulting in interpersonal
isolation, these measures have raised serious concerns for mental
health and depression. Generally, clinical psychologists diagnose
depressed people via face-to-face interviews following the clinical
depression criteria. However, often patients tend to not consult
doctors in their early stages of depression. Nowadays, people are
increasingly using social media to express their moods. In this
article, we aim to predict depressed users as well as estimate
their depression intensity via leveraging social media (Twitter)
data, in order to aid in raising an alarm. We model this
problem as a supervised learning task. We start with weakly
labeling the Twitter data in a self-supervised manner. A rich
set of features, including emotional, topical, behavioral, user
level, and depression-related n-gram features, are extracted to
represent each user. Using these features, we train a small
long short-term memory (LSTM) network using Swish as an
activation function, to predict the depression intensities. We perform extensive experiments to demonstrate the efficacy of our
method. We outperform the baseline models for depression
intensity estimation by achieving the lowest mean squared error
of 1.42 and also outperform the existing state-of-the-art binary
classification method by more than 2% of accuracy. We found
that the depressed users frequently use negative words such as
stress and sad, mostly post during late nights, highly use personal
pronouns and sometimes also share personal events.
