Monitoring Tweets for Depression to Detect At-risk Users
PRESENTER:
Zunaira Jamil
University of Ottawa
ABSTRACT:
According to the World Health Organization, mental health is an integral
part of health and well-being. Mental illness can affect anyone, rich or
poor, male or female. One such example of mental illness is depression. In
Canada 5.3% of the population had presented a depressive episode in the
past 12 months. Depression is difficult to diagnose, resulting in high
under-diagnosis. Diagnosing depression is often based on self-reported
experiences, behaviors reported by relatives, and a mental status
examination. Currently, authorities use surveys and questionnaires to
identify individuals who may be at risk of depression. This process is
time-consuming and costly. We propose an automated system that can
identify at-risk users from their public social media activity. More
specifically, we identify at-risk users from Twitter. To achieve this goal
we trained a user-level classier using Support Vector Machine (SVM) that
can detect at-risk users with a recall of 0.8750 and a precision of
0.7778. We also trained a tweet-level classier that predicts if a tweet
indicates distress. This task was much more di cult due to the imbalanced
data. In the dataset that we labeled, we came across 5% distress tweets
and 95% non-distress tweets. To handle this class imbalance, we used
undersampling methods. The resulting classier uses SVM and performs with a
recall of 0.8020 and a precision of 0.1237. Our system can be used by
authorities to send a focused group of at-risk users. It is not a platform
for labeling an individual as a patient with depression, but only a
platform for raising an alarm so that the relevant authorities could take
necessary interventions to further analyze the predicted user to confirm
his/her state of mental health. We respect the ethical boundaries relating
to the use of social media data and therefore do not use any user
identification information in our research.