Semi-Supervised approach to monitoring clinical depressive symptoms in social media

Amir Hossein Yazdavar; Hussein S. Al-Olimat; Monireh Ebrahimi; Goonmeet Bajaj; Tanvi Banerjee; Krishnaprasad Thirunarayan; Jyotishman Pathak; Amit Sheth

doi:10.1145/3110025.3123028

Semi-Supervised approach to monitoring clinical depressive symptoms in social media

Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth

Quantitative Health Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

34 Scopus citations

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%.

Original language	English (US)
Title of host publication	Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
Editors	Jana Diesner, Elena Ferrari, Guandong Xu
Publisher	Association for Computing Machinery, Inc
Pages	1191-1198
Number of pages	8
ISBN (Electronic)	9781450349932
DOIs	https://doi.org/10.1145/3110025.3123028
State	Published - Jul 31 2017
Event	9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 - Sydney, Australia Duration: Jul 31 2017 → Aug 3 2017

Publication series

Name	Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

Other

Other	9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
Country/Territory	Australia
City	Sydney
Period	7/31/17 → 8/3/17

Keywords

Mental health
Natural language processing
Semi-supervised machine learning
Social media

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems

Access to Document

10.1145/3110025.3123028

Cite this

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. In J. Diesner, E. Ferrari, & G. Xu (Eds.), Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 (pp. 1191-1198). (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017). Association for Computing Machinery, Inc. https://doi.org/10.1145/3110025.3123028

Semi-Supervised approach to monitoring clinical depressive symptoms in social media. / Yazdavar, Amir Hossein; Al-Olimat, Hussein S.; Ebrahimi, Monireh et al.
Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. ed. / Jana Diesner; Elena Ferrari; Guandong Xu. Association for Computing Machinery, Inc, 2017. p. 1191-1198 (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yazdavar, AH, Al-Olimat, HS, Ebrahimi, M, Bajaj, G, Banerjee, T, Thirunarayan, K, Pathak, J & Sheth, A 2017, Semi-Supervised approach to monitoring clinical depressive symptoms in social media. in J Diesner, E Ferrari & G Xu (eds), Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, Association for Computing Machinery, Inc, pp. 1191-1198, 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, Sydney, Australia, 7/31/17. https://doi.org/10.1145/3110025.3123028

Yazdavar AH, Al-Olimat HS, Ebrahimi M, Bajaj G, Banerjee T, Thirunarayan K et al. Semi-Supervised approach to monitoring clinical depressive symptoms in social media. In Diesner J, Ferrari E, Xu G, editors, Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. Association for Computing Machinery, Inc. 2017. p. 1191-1198. (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017). doi: 10.1145/3110025.3123028

Yazdavar, Amir Hossein ; Al-Olimat, Hussein S. ; Ebrahimi, Monireh et al. / Semi-Supervised approach to monitoring clinical depressive symptoms in social media. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. editor / Jana Diesner ; Elena Ferrari ; Guandong Xu. Association for Computing Machinery, Inc, 2017. pp. 1191-1198 (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017).

@inproceedings{8519fde2f7e9479ba0675c0e6c4f6fd0,

title = "Semi-Supervised approach to monitoring clinical depressive symptoms in 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%.",

keywords = "Mental health, Natural language processing, Semi-supervised machine learning, Social media",

author = "Yazdavar, {Amir Hossein} and Al-Olimat, {Hussein S.} and Monireh Ebrahimi and Goonmeet Bajaj and Tanvi Banerjee and Krishnaprasad Thirunarayan and Jyotishman Pathak and Amit Sheth",

note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.; 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 ; Conference date: 31-07-2017 Through 03-08-2017",

year = "2017",

month = jul,

day = "31",

doi = "10.1145/3110025.3123028",

language = "English (US)",

series = "Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017",

publisher = "Association for Computing Machinery, Inc",

pages = "1191--1198",

editor = "Jana Diesner and Elena Ferrari and Guandong Xu",

booktitle = "Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017",

}

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

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 -

Semi-Supervised approach to monitoring clinical depressive symptoms in social media

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this