  Depression is the leading cause of disability worldwide. Initial efforts to
detect depression signals from social media posts have shown promising results.
Given the high internal validity, results from such analyses are potentially
beneficial to clinical judgment. The existing models for automatic detection of
depressive symptoms learn proxy diagnostic signals from social media data, such
as help-seeking behavior for mental health or medication names. However, in
reality, individuals with depression typically experience depressed mood, loss
of pleasure nearly in all the activities, feeling of worthlessness or guilt,
and diminished ability to think. Therefore, a lot of the proxy signals used in
these models lack the theoretical underpinnings for depressive symptoms. It is
also reported that social media posts from many patients in the clinical
setting do not contain these signals. Based on this research gap, we propose to
monitor a type of signal that is well-established as a class of symptoms in
affective disorders -- mood. The mood is an experience of feeling that can last
for hours, days, or even weeks. In this work, we attempt to enrich current
technology for detecting symptoms of potential depression by constructing a
'mood profile' for social media users.
