TY - GEN
T1 - "Let me tell you about your mental health!" Contextualized classification of reddit posts to DSM-5 for web-based intervention
AU - Gaur, Manas
AU - Sheth, Amit
AU - Kursuncu, Ugur
AU - Daniulaityte, Raminta
AU - Pathak, Jyotishman
AU - Alambo, Amanuel
AU - Thirunarayan, Krishnaprasad
N1 - Funding Information:
We acknowledge partial support from the National Science Foundation (NSF) award CNS-1513721: Context-Aware Harassment Detection on Social Media", National Institutes of Health (NIH) award: MH105384-01A1: Modeling Social Behavior for Healthcare Utilization in Depression", and National Institute on Drug Abuse (NIDA) Grant No. 5R01DA039454-02 Trending: Social media analysis to monitor cannabis and synthetic cannabinoid use . Any opinions, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF, NIH, or NIDA.
Funding Information:
We acknowledge partial support from the National Science Foundation (NSF) award CNS-1513721: “Context-Aware Harassment Detection on Social Media", National Institutes of Health (NIH) award: MH105384-01A1: “Modeling Social Behavior for Healthcare Utilization in Depression", and National Institute on Drug Abuse (NIDA) Grant No. 5R01DA039454-02 “Trending: Social media analysis to monitor cannabis and synthetic cannabinoid use”. Any opinions, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF, NIH, or NIDA.
Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/10/17
Y1 - 2018/10/17
N2 - Social media platforms are increasingly being used to share and seek advice on mental health issues. In particular, Reddit users freely discuss such issues on various subreddits, whose structure and content can be leveraged to formally interpret and relate subreddits and their posts in terms of mental health diagnostic categories. There is prior research on the extraction of mental health-related information, including symptoms, diagnosis, and treatments from social media; however, our approach can additionally provide actionable information to clinicians about the mental health of a patient in diagnostic terms for web-based intervention. Specifically, we provide a detailed analysis of the nature of subreddit content from domain expert's perspective and introduce a novel approach to map each subreddit to the best matching DSM-5 (Diagnostic and Statistical Manual of Mental Disorders - 5th Edition) category using multiclass classifier. Our classification algorithm analyzes all the posts of a subreddit by adapting topic modeling and word-embedding techniques, and utilizing curated medical knowledge bases to quantify relationship to DSM-5 categories. Our semantic encoding-decoding optimization approach reduces the false-alarm-rate from 30% to 2.5% over a comparable heuristic baseline, and our mapping results have been verified by domain experts achieving a kappa score of 0.84.
AB - Social media platforms are increasingly being used to share and seek advice on mental health issues. In particular, Reddit users freely discuss such issues on various subreddits, whose structure and content can be leveraged to formally interpret and relate subreddits and their posts in terms of mental health diagnostic categories. There is prior research on the extraction of mental health-related information, including symptoms, diagnosis, and treatments from social media; however, our approach can additionally provide actionable information to clinicians about the mental health of a patient in diagnostic terms for web-based intervention. Specifically, we provide a detailed analysis of the nature of subreddit content from domain expert's perspective and introduce a novel approach to map each subreddit to the best matching DSM-5 (Diagnostic and Statistical Manual of Mental Disorders - 5th Edition) category using multiclass classifier. Our classification algorithm analyzes all the posts of a subreddit by adapting topic modeling and word-embedding techniques, and utilizing curated medical knowledge bases to quantify relationship to DSM-5 categories. Our semantic encoding-decoding optimization approach reduces the false-alarm-rate from 30% to 2.5% over a comparable heuristic baseline, and our mapping results have been verified by domain experts achieving a kappa score of 0.84.
KW - DSM-5
KW - Drug Abuse Ontology
KW - Medical Knowledge bases
KW - Mental Health
KW - Reddit
KW - Semantic Encoding and Decoding
KW - Semantic Social Computing
UR - http://www.scopus.com/inward/record.url?scp=85058009561&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058009561&partnerID=8YFLogxK
U2 - 10.1145/3269206.3271732
DO - 10.1145/3269206.3271732
M3 - Conference contribution
AN - SCOPUS:85058009561
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 753
EP - 762
BT - CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
A2 - Paton, Norman
A2 - Candan, Selcuk
A2 - Wang, Haixun
A2 - Allan, James
A2 - Agrawal, Rakesh
A2 - Labrinidis, Alexandros
A2 - Cuzzocrea, Alfredo
A2 - Zaki, Mohammed
A2 - Srivastava, Divesh
A2 - Broder, Andrei
A2 - Schuster, Assaf
PB - Association for Computing Machinery
T2 - 27th ACM International Conference on Information and Knowledge Management, CIKM 2018
Y2 - 22 October 2018 through 26 October 2018
ER -