Nonparametric discovery of online mental health-related communities Dao, Bo, Nguyen, Thin, Venkatesh, Svetha and Phung, Dinh 2015, Nonparametric discovery of online mental health-related communities, in DSAA 2015: Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, IEEE, Piscataway, N.J., pp. 1-10, doi: 10.1109/DSAA.2015.7344859. Attached Files Name Description MIMEType Size Downloads Title Nonparametric discovery of online mental health-related communities Author(s) Dao, Bo Nguyen, Thin orcid.org/0000-0003-3467-8963 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Phung, Dinh orcid.org/0000-0002-9977-8247 Conference name Data Science and Advanced Analytics. Conference (2015 : Paris, France) Conference location Paris, France Conference dates 19-21 Oct. 2015 Title of proceedings DSAA 2015: Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics Editor(s) Gaussier, Eric Cao, Longbing Gallinari, Patrick Kwok, James Pasi, Gabriella Zaiane, Osmar Publication date 2015 Start page 1 End page 10 Total pages 10 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) nonparametric discovery Online communities mental Health moods and emotion topics social media Summary People are increasingly using social media, especially online communities, to discuss mental health issues and seek supports. Understanding topics, interaction, sentiment and clustering structures of these communities informs important aspects of mental health. It can potentially add knowledge to the underlying cognitive dynamics, mood swings patterns, shared interests, and interaction. There has been growing research interest in analyzing online mental health communities; however sentiment analysis of these communities has been largely under-explored. This study presents an analysis of online Live Journal communities with and without mental health-related conditions including depression and autism. Latent topics for mood tags, affective words, and generic words in the content of the posts made in these communities were learned using nonparametric topic modelling. These representations were then input into a nonparametric clustering to discover meta-groups among the communities. The best performance results can be achieved on clustering communities with latent mood-based representation for such communities. The study also found significant differences in usage latent topics for mood tags and affective features between online communities with and without affective disorders. The findings reveal useful insights into hyper-group detection of online mental health-related communities. ISBN 9781467382724 Language eng DOI 10.1109/DSAA.2015.7344859 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 ©2015, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30081984 Document type: Conference Paper Collections: School of Information Technology Centre for Pattern Recognition and Data Analytics 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 Mon, 07 Mar 2016, 17:20:42 EST Mon, 07 Mar 2016, 17:21:31 EST Tue, 08 Mar 2016, 04:05:27 EST Tue, 08 Mar 2016, 05:10:09 EST Tue, 12 Apr 2016, 08:49:58 EST Tue, 12 Apr 2016, 08:52:04 EST Fri, 07 Oct 2016, 14:00:34 EST Mon, 27 Feb 2017, 13:13:27 EST Sat, 11 Mar 2017, 01:30:06 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 300 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Mon, 07 Mar 2016, 17:21:31 EST

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