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Online social capital : mood, topical and psycholinguistic analysis

Nguyen, Thin, Dao, Bo, Phung, Dinh, Venkatesh, Svetha and Berk, Michael 2013, Online social capital : mood, topical and psycholinguistic analysis, in ICWSM 2013 : Proceedings of the 7th AAAI International Conference on Weblogs and Social Media, AAAI Press, Palo Alto, Calif., pp. 449-456.

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Title Online social capital : mood, topical and psycholinguistic analysis
Author(s) Nguyen, Thin
Dao, Bo
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, Svetha
Berk, MichaelORCID iD for Berk, Michael orcid.org/0000-0002-5554-6946
Conference name Weblogs and Social Media. AAAI International Conference (7th : 2013 : Cambridge, Massachusetts)
Conference location Cambridge, Massachusetts
Conference dates 8-11 Jul. 2013
Title of proceedings ICWSM 2013 : Proceedings of the 7th AAAI International Conference on Weblogs and Social Media
Editor(s) [Unknown]
Publication date 2013
Conference series AAAI International Conference on Weblogs and Social Media
Start page 449
End page 456
Total pages 8
Publisher AAAI Press
Place of publication Palo Alto, Calif.
Summary Social media provides rich sources of personal information and community interaction which can be linked to aspect of mental health. In this paper we investigate manifest properties of textual messages, including latent topics, psycholinguistic features, and authors' mood, of a large corpus of blog posts, to analyze the aspect of social capital in social media communities. Using data collected from Live Journal, we find that bloggers with lower social capital have fewer positive moods and more negative moods than those with higher social capital. It is also found that people with low social capital have more random mood swings over time than the people with high social capital. Significant differences are found between low and high social capital groups when characterized by a set of latent topics and psycholinguistic features derived from blogposts, suggesting discriminative features, proved to be useful for classification tasks. Good prediction is achieved when classifying among social capital groups using topic and linguistic features, with linguistic features are found to have greater predictive power than latent topics. The significance of our work lies in the importance of online social capital to potential construction of automatic healthcare monitoring systems. We further establish the link between mood and social capital in online communities, suggesting the foundation of new systems to monitor online mental well-being.
ISBN 9781577356103
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 920410 Mental Health
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057170

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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.