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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.

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Title Discovering latent affective dynamics among individuals in online mental health-related communities
Author(s) Dao, Bo
Nguyen, Thin
Venkatesh, Svetha
Phung, Quoc-DinhORCID iD for 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
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Created: Thu, 20 Oct 2016, 12:22:14 EST

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