A study of preferences for sharing and privacy
CHI '05 Extended Abstracts on Human Factors in Computing Systems
ACM Transactions on Computer-Human Interaction (TOCHI)
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Analysis of a Location-Based Social Network
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Imagined communities: awareness, information sharing, and privacy on the facebook
PET'06 Proceedings of the 6th international conference on Privacy Enhancing Technologies
Attention please!: learning analytics for visualization and recommendation
Proceedings of the 1st International Conference on Learning Analytics and Knowledge
Public health community mining in YouTube
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Sentiment lexicons for health-related opinion mining
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Temporal Community Structure Patterns in Diabetes Social Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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With the continued advances of Web 2.0, health-centered Online Social Networks (OSNs) are emerging to provide knowledge and support for those interested in managing their own health. Despite the success of the OSNs for better connecting the users through sharing statuses, photos, blogs, and so on, it is unclear how the users are willing to share health related information and whether these specialpurpose OSNs can actually change the users' health behaviors to become more healthy. This paper provides an empirical analysis of a health OSN, which allows its users to record their foods and exercises, to track their diet progress towards weight-change goals, and to socialize and group with each other for community support. Based on about five month data collected from more than 107,000 users, we studied their weigh-in behaviors and tracked their weight-change progress. We found that the users' weight changes correlated positively with the number of their friends and their friends' weight-change performance. We also show that the users' weight changes have rippling effects in the OSN due to the social influence. The strength of such online influence and its propagation distance appear to be greater than those in the real-world social network. To the best of our knowledge, this is the first detailed study of a large-scale modern health OSN.