Paper Info
Title
Text-Based Detection and Understanding of Changes in Mental Health.
Abstract
Previous work has investigated the identification of mental health issues in social media users, yet the way that users' mental states and related behavior change over time remains relatively understudied. This paper focuses on online mental health communities and studies how users' contributions to these communities change over one year. We define a metric called the Mental Health Contribution Index (MHCI), which we use to measure the degree to which users' contributions to mental health topics change over a one-year period. In this work, we study the relationship between MHCI scores and the online expression of mental health symptoms by extracting relevant linguistic features from user-generated content and conducting statistical analyses. Additionally, we build a classifier to predict whether or not a user's contributions to mental health subreddits will increase or decrease. Finally, we employ propensity score matching to identify factors that correlate with an increase or a decrease in mental health forum contributions. Our work provides some of the first insights into detecting and understanding social media users' changes in mental health states over time.
Year
DOI
Venue
2018
10.1007/978-3-030-01159-8_17
Lecture Notes in Computer Science
Keywords
Field
DocType
Natural language processing,Mental health,Social media
Internet privacy,Social media,Propensity score matching,Computer science,Mental health,Classifier (linguistics),Applied psychology,Behavior change
Conference
Volume
ISSN
Citations 
11186
0302-9743
2
PageRank 
References 
Authors
0.38
24
3
Name
Order
Citations
PageRank
Yaoyiran Li120.38
Rada Mihalcea26460445.54
Steven R. Wilson3127.21