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. Attached Files Name Description MIMEType Size Downloads venkatesh-onlinsocial-2013.pdf Published version application/pdf 146.00KB 1104 Title Online social capital : mood, topical and psycholinguistic analysis Author(s) Nguyen, Thin orcid.org/0000-0003-3467-8963 Dao, Bo Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 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 Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30057170 Document type: Conference Paper Collections: Faculty of Health School of Medicine Faculty of Science, Engineering and Built Environment Centre for Pattern Recognition and Data Analytics (PRADA) Open Access Collection 2018 ERA Submission   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Wed, 23 Oct 2013, 10:03:43 EST Wed, 23 Oct 2013, 15:11:30 EST Wed, 06 Nov 2013, 10:20:54 EST Wed, 15 Jan 2014, 13:10:42 EST Thu, 20 Feb 2014, 07:37:52 EST Tue, 29 Apr 2014, 17:01:08 EST Wed, 30 Apr 2014, 12:54:40 EST Wed, 30 Apr 2014, 12:57:25 EST Wed, 30 Apr 2014, 12:58:08 EST Wed, 21 Oct 2015, 14:52:48 EST Mon, 27 Feb 2017, 13:14:53 EST Wed, 06 Dec 2017, 10:39:57 EST Fri, 07 Sep 2018, 17:42:44 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 5 times in Scopus Search Google Scholar Access Statistics: 861 Abstract Views, 1132 File Downloads  -  Detailed Statistics Created: Wed, 23 Oct 2013, 10:03:43 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 drosupport@deakin.edu.au.