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Discovering latent affective dynamics among individuals in online mental health-related communities
conference contribution
posted on 2016-08-01, 00:00 authored by B Dao, Thin NguyenThin Nguyen, Svetha VenkateshSvetha Venkatesh, Quoc-Dinh PhungDiscovering 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.
History
Event
IEEE Multimedia & Expo. International Conference (2016 : Seattle, Washington)Pagination
473 - 478Publisher
IEEELocation
Seattle, WashingtonPlace of publication
Piscataway, N. J.Publisher DOI
Start date
2016-07-11End date
2016-07-15ISSN
1945-7871eISSN
1945-788XISBN-13
9781467372589Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2016, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
ICME 2016 : Proceedings of the IEEE Multimedia and Expo International ConferenceUsage metrics
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