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A Framework for Classifying Online Mental Health-Related Communities With an Interest in Depression.

Abstract：

:Mental illness has a deep impact on individuals, families, and by extension, society as a whole. Social networks allow individuals with mental disorders to communicate with others sufferers via online communities, providing an invaluable resource for studies on textual signs of psychological health problems. Mental disorders often occur in combinations, e.g., a patient with an anxiety disorder may also develop depression. This co-occurring mental health condition provides the focus for our work on classifying online communities with an interest in depression. For this, we have crawled a large body of 620 000 posts made by 80 000 users in 247 online communities. We have extracted the topics and psycholinguistic features expressed in the posts, using these as inputs to our model. Following a machine learning technique, we have formulated a joint modeling framework in order to classify mental health-related co-occurring online communities from these features. Finally, we performed empirical validation of the model on the crawled dataset where our model outperforms recent state-of-the-art baselines.

journal_name

IEEE J Biomed Health Inform

journal_title

IEEE journal of biomedical and health informatics

authors

Saha B,Nguyen T,Phung D,Venkatesh S

doi

10.1109/JBHI.2016.2543741

subject

Has Abstract

pub_date

2016-07-01 00:00:00

pages

1008-15

issue

4

eissn

2168-2194

issn

2168-2208

journal_volume

20

pub_type

杂志文章

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