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

Authors

Amir Hossein Yazdavar, Wright State University - Main Campus
Hussein S. Al-Olimat, Wright State University - Main Campus
Monireh Ebrahimi, Wright State University - Main Campus
Goonmeet Bajaj
Tanvi Banerjee, Wright State University - Main CampusFollow
Krishnaprasad Thirunarayan, Wright State University - Main CampusFollow
Jyotishman Pathak
Amit Sheth, Wright State University - Main CampusFollow

Document Type

Article

Publication Date

1-1-2017

Identifier/URL

136361411 (Orcid)

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%.

Comments

Accepted by 2017 IEEE/ACM Internatonal Conference

Repository Citation

Yazdavar, A. H., Al-Olimat, H. S., Ebrahimi, M., Bajaj, G., Banerjee, T., Thirunarayan, K., Pathak, J., & Sheth, A. (2017). Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media. .
https://corescholar.libraries.wright.edu/cse/652