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Analysis of circadian rhythms from online communities of individuals with affective disorders

Dao,B, Nguyen,T, Venkatesh,S and Phung,D 2014, Analysis of circadian rhythms from online communities of individuals with affective disorders, in DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics, IEEE, Piscataway, N.J., pp. 463-469, doi: 10.1109/DSAA.2014.7058113.

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Title Analysis of circadian rhythms from online communities of individuals with affective disorders
Author(s) Dao,B
Nguyen,T
Venkatesh,S
Phung,DORCID iD for Phung,D orcid.org/0000-0002-9977-8247
Conference name Data Science and Advanced Analystics. Conference (2010 : Shanghai, China)
Conference location Shanghai, China
Conference dates 2014/10/30 - 2014/11/1
Title of proceedings DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics
Editor(s) [Unknown]
Publication date 2014
Conference series IEEE International Conference on Data Science and Advanced Analytics
Start page 463
End page 469
Total pages 7
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) affective disorders
circadian rhythms
online communities
psycholinguistics
social media
Summary The circadian system regulates 24 hour rhythms in biological creatures. It impacts mood regulation. The disruptions of circadian rhythms cause destabilization in individuals with affective disorders, such as depression and bipolar disorders. Previous work has examined the role of the circadian system on effects of light interactions on mood-related systems, the effects of light manipulation on brain, the impact of chronic stress on rhythms. However, such studies have been conducted in small, preselected populations. The deluge of data is now changing the landscape of research practice. The unprecedented growth of social media data allows one to study individual behavior across large and diverse populations. In particular, individuals with affective disorders from online communities have not been examined rigorously. In this paper, we aim to use social media as a sensor to identify circadian patterns for individuals with affective disorders in online communities.We use a large scale study cohort of data collecting from online affective disorder communities. We analyze changes in hourly, daily, weekly and seasonal affect of these clinical groups in contrast with control groups of general communities. By comparing the behaviors between the clinical groups and the control groups, our findings show that individuals with affective disorders show a significant distinction in their circadian rhythms across the online activity. The results shed light on the potential of using social media for identifying diurnal individual variation in affective state, providing key indicators and risk factors for noninvasive wellbeing monitoring and prediction.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 9781479969913
Language eng
DOI 10.1109/DSAA.2014.7058113
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 ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072737

Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
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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 drosupport@deakin.edu.au.