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.
History
Volume
7
Pagination
449-456
Location
Cambridge, Massachusetts
Start date
2013-07-08
End date
2013-07-11
ISSN
2162-3449
eISSN
2334-0770
ISBN-13
9781577356103
Language
eng
Publication classification
E1 Full written paper - refereed
Copyright notice
2013, The Authors
Title of proceedings
ICWSM 2013 : Proceedings of the 7th AAAI International Conference on Weblogs and Social Media
Event
Weblogs and Social Media. AAAI International Conference (7th : 2013 : Cambridge, Massachusetts)