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, ThinORCID iD for Nguyen, Thin
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Phung, DinhORCID iD for Phung, Dinh
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
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

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 540 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Mon, 07 Mar 2016, 17:21:31 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact