Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

TitleSemi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media
Publication TypeConference Paper
Year of Publication2017
AuthorsAmir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth
Conference NameAdvances in Social Networks Analysis and Mining (ASONAM)
PublisherAdvances in Social Networks Analysis and Mining (ASONAM), 2017 IEEE/ACM International Conferance on
Conference LocationSydney, Australia
KeywordsDepression, Mental Health, Natural Language Processing, Semi-supervised Machine Learning, Social Media

Abstract—With the rise of social media, millions of people
are routinely expressing their moods, feelings, and daily struggles
with mental health issues on social media platforms like
Twitter. Unlike traditional observational cohort studies conducted
through questionnaires and self-reported surveys, we explore the
reliable detection of clinical depression from tweets obtained
unobtrusively. Based on the analysis of tweets crawled from users
with self-reported depressive symptoms in their Twitter profiles,
we demonstrate the potential for detecting clinical depression
symptoms which emulate the PHQ-9 questionnaire clinicians
use today. Our study uses a semi-supervised statistical model
to evaluate how the duration of these symptoms and their
expression on Twitter (in terms of word usage patterns and
topical preferences) align with the medical findings reported via
the PHQ-9. Our proactive and automatic screening tool is able to
identify clinical depressive symptoms with an accuracy of 68%
and precision of 72%.

Modeling Social Behavior for Healthcare Utilization in Depression
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