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A framework for classifying online mental health related communities with an interest in depression

Saha, Budhaditya, Nguyen, Thin, Phung, Dinh and Venkatesh, Svetha 2016, A framework for classifying online mental health related communities with an interest in depression, IEEE journal of biomedical and health informatics, vol. 20, no. 4, pp. 1008-1015, doi: 10.1109/JBHI.2016.2543741.

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Title A framework for classifying online mental health related communities with an interest in depression
Author(s) Saha, BudhadityaORCID iD for Saha, Budhaditya orcid.org/0000-0001-8011-6801
Nguyen, ThinORCID iD for Nguyen, Thin orcid.org/0000-0003-3467-8963
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Journal name IEEE journal of biomedical and health informatics
Volume number 20
Issue number 4
Start page 1008
End page 1015
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2016-07
ISSN 2168-2194
2168-2208
Summary 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 psycho-linguistic features expressed in the posts, using these as inputs to our model. Following a machine learning technique, we have formulated a joint modelling 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.
Language eng
DOI 10.1109/JBHI.2016.2543741
Field of Research 080702 Health Informatics
080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082141

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.