Paper Info
Title
Detecting depressed users in online forums.
Abstract
Depression is the most common mental illness in the U.S., with 6.7% of all adults who have experienced a major depressive episode. Unfortunately, depression extends to teens and young users as well, and researchers observed an increasing rate in the recent years (from 8.7% in 2005 to 11.3% in 2014 in adolescents and from 8.8% to 9.6% in young adults), especially among girls and women. People themselves are a barrier to fight this disease as they tend to hide their symptoms and do not receive treatments. However, protected by anonymity, they share their sentiments on the Web, looking for help. In this paper, we address the problem of detecting depressed users in online forums. We analyze user behavior in the ReachOut.com online forum, a platform providing a supportive environment for young people to discuss their everyday issues, including depression. We examine the linguistic style of user posts in combination with network-based features modeling how users connect in the forum. Our results show that network features are strong predictors of depressed users and, by combining them with user post linguistic features, we can achieve an average precision of 0.78 (vs. 0.47 of a random classifier and 0.71 of linguistic features only) and perform better than related work (F1-measure of 0.63 vs. 0.50).
Year
DOI
Venue
2019
10.1145/3341161.3343511
ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining Vancouver British Columbia Canada August, 2019
Keywords
Field
DocType
Depression, Online forums, Social network analysis, Linguistic analysis
World Wide Web,Computer science,Artificial intelligence,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-6868-1
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Anu Shrestha101.69
Francesca Spezzano28019.08