TY - GEN
T1 - Semi-Supervised approach to monitoring clinical depressive symptoms in social media
AU - Yazdavar, Amir Hossein
AU - Al-Olimat, Hussein S.
AU - Ebrahimi, Monireh
AU - Bajaj, Goonmeet
AU - Banerjee, Tanvi
AU - Thirunarayan, Krishnaprasad
AU - Pathak, Jyotishman
AU - Sheth, Amit
N1 - Funding Information:
We are thankful to Surendra Marupudi and Ankita Saxena for helping us with data collection. We also thank Jibril Ikharo for his proofreading. Research reported in this publication was supported in part by NIMH of the National Institutes of Health (NIH) under award number R01MH105384-01A1.
Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/7/31
Y1 - 2017/7/31
N2 - 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%.
AB - 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%.
KW - Mental health
KW - Natural language processing
KW - Semi-supervised machine learning
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=85040237917&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040237917&partnerID=8YFLogxK
U2 - 10.1145/3110025.3123028
DO - 10.1145/3110025.3123028
M3 - Conference contribution
AN - SCOPUS:85040237917
T3 - Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
SP - 1191
EP - 1198
BT - Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
A2 - Diesner, Jana
A2 - Ferrari, Elena
A2 - Xu, Guandong
PB - Association for Computing Machinery, Inc
T2 - 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
Y2 - 31 July 2017 through 3 August 2017
ER -