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Differentiating sub-groups of online depression-related communities using textual cues
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posted on 2015-12-18, 00:00 authored by Thin NguyenThin Nguyen, B O Dea, M Larsen, Quoc-Dinh Phung, Svetha VenkateshSvetha Venkatesh, H ChristensenDepression 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.
History
Title of book
Web Information Systems Engineering – WISE 2015Volume
9419Series
Lecture notes in computer scienceChapter number
17Pagination
216 - 224Publisher
SpringerPlace of publication
New York, N.Y.Publisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319261867Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2015, SpringerExtent
26Editor/Contributor(s)
J Wang, W Cellary, D Wang, H Wang, S Chen, T Li, Y ZhangUsage metrics
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