Discovering latent affective dynamics among individuals in online mental health-related communities Dao, Bo, Nguyen, Thin, Venkatesh, Svetha and Phung, Quoc-Dinh 2016, Discovering latent affective dynamics among individuals in online mental health-related communities, in ICME 2016 : Proceedings of the IEEE Multimedia and Expo International Conference, IEEE, Piscataway, N. J., pp. 473-478, doi: 10.1109/ICME.2016.7552947. Attached Files Name Description MIMEType Size Downloads Title Discovering latent affective dynamics among individuals in online mental health-related communities Author(s) Dao, Bo Nguyen, Thin Venkatesh, Svetha Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247 Conference name IEEE Multimedia & Expo. International Conference (2016 : Seattle, Washington) Conference location Seattle, Washington Conference dates 11-15 Jul. 2016 Title of proceedings ICME 2016 : Proceedings of the IEEE Multimedia and Expo International Conference Editor(s) [Unknown] Publication date 2016 Conference series IEEE Multimedia & Expo International Conference Start page 473 End page 478 Total pages 6 Publisher IEEE Place of publication Piscataway, N. J. Keyword(s) joint factor analysis nonnegative matrix factorization affective transition mental health online communities Summary Discovering dynamics of emotion and mood changes for individuals has the potential to enhance the diagnosis and treatment of mental disorders. In this paper we study affective transitions and dynamics among individuals in online mental health communities. Using social media as form of 'sensor', we crawl a large dataset of blogs posted by online communities whose descriptions declared to be associated with affective disorder conditions such as depression, anxiety, or autism. We then apply nonnegative matrix factorization model to extract the common and individual factors of affective transitions across groups of individuals in different levels of affective disorders. We examine the latent patterns of emotional transitions and investigate the effects of emotional transitions across the cohorts. Our framework is novel as it utilizes social media as an online sensing platform of mood and emotional dynamics. Hence, our work has implication in constructing systems to screen individuals and communities at high risks of mental health problems in online settings. ISBN 9781467372589 ISSN 1945-7871 1945-788X Language eng DOI 10.1109/ICME.2016.7552947 Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©2016, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30086969 Document type: Conference Paper Collection: Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges   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 Tue, 11 Oct 2016, 12:51:36 EST Thu, 20 Oct 2016, 12:22:14 EST Wed, 26 Oct 2016, 14:54:30 EST Thu, 27 Oct 2016, 09:23:28 EST Mon, 14 Nov 2016, 08:55:36 EST Mon, 14 Nov 2016, 09:00:11 EST Mon, 14 Nov 2016, 09:01:30 EST Wed, 11 Jan 2017, 09:54:14 EST Mon, 27 Feb 2017, 13:13:24 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 84 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Thu, 20 Oct 2016, 12:22:14 EST

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