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Differentiating sub-groups of online depression-related communities using textual cues

Nguyen, Thin, O Dea, Bridianne, Larsen, Mark, Phung, Dinh, Venkatesh, Svetha and Christensen, Helen 2015, Differentiating sub-groups of online depression-related communities using textual cues. In Wang, Jianyong, Cellary, Wojciech, Wang, Dingding, Wang, Hua, Chen, Shu-Ching, Li, Tao and Zhang, Yanchun (ed), Web Information Systems Engineering – WISE 2015, Springer, New York, N.Y., pp.216-224, doi: 10.1007/978-3-319-26187-4_17.

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Title Differentiating sub-groups of online depression-related communities using textual cues
Author(s) Nguyen, ThinORCID iD for Nguyen, Thin orcid.org/0000-0003-3467-8963
O Dea, Bridianne
Larsen, Mark
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Christensen, Helen
Title of book Web Information Systems Engineering – WISE 2015
Editor(s) Wang, Jianyong
Cellary, Wojciech
Wang, Dingding
Wang, Hua
Chen, Shu-Ching
Li, Tao
Zhang, Yanchun
Publication date 2015
Series Lecture notes in computer science; v. 9419
Chapter number 17
Total chapters 26
Start page 216
End page 224
Total pages 9
Publisher Springer
Place of Publication New York, N.Y.
Keyword(s) web community
feature extraction
textual cues
online depression
Summary Depression is a highly prevalent mental illness and is a comorbidity of other mental and behavioural disorders. The Internet allows individuals who are depressed or caring for those who are depressed, to connect with others via online communities; however, the characteristics of these online conversations and the language styles of those interested in depression have not yet been fully explored. This work aims to explore the textual cues of online communities interested in depression. A random sample of 5,000 blog posts was crawled. Five groupings were identified: depression, bipolar, self-harm, grief, and suicide. Independent variables included psycholinguistic processes and content topics extracted from the posts. Machine learning techniques were used to discriminate messages posted in the depression sub-group from the others.Good predictive validity in depression classification using topics and psycholinguistic clues as features was found. Clear discrimination between writing styles and content, with good predictive power is an important step in understanding social media and its use in mental health.
ISBN 9783319261867
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-26187-4_17
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082629

Document type: Book Chapter
Collection: Centre for Pattern Recognition and Data Analytics
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