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. Attached Files Name Description MIMEType Size Downloads Title Differentiating sub-groups of online depression-related communities using textual cues Author(s) Nguyen, Thin orcid.org/0000-0003-3467-8963 O Dea, Bridianne Larsen, Mark Phung, Dinh orcid.org/0000-0002-9977-8247 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 Collections: Faculty of Science, Engineering and Built Environment Centre for Pattern Recognition and Data Analytics (PRADA) Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 08 Apr 2016, 12:50:04 EST Thu, 14 Apr 2016, 11:21:03 EST Tue, 17 May 2016, 12:46:06 EST Wed, 18 May 2016, 05:00:19 EST Wed, 18 May 2016, 06:25:07 EST Thu, 02 Jun 2016, 15:52:32 EST Thu, 02 Jun 2016, 15:53:14 EST Tue, 13 Mar 2018, 13:46:30 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 4 times in Scopus Search Google Scholar Access Statistics: 559 Abstract Views, 7 File Downloads  -  Detailed Statistics Created: Tue, 17 May 2016, 12:46:06 EST

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.