Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - What makes communities tick?
T2 - IEEE Conference on Social Computing 2012
AU - Rowe, Matthew
AU - Alani, Harith
PY - 2012/9/1
Y1 - 2012/9/1
N2 - Today's Web is social and largely driven by a wide variety of online communities. Many such communities are owned and managed by businesses that draw much value from these communities, in the form of efficient and cheaper customer support, generation of new ideas, fast spreading of information, etc. Understanding how to measure the health of online communities and how to predict its change over time, whether to better or to worse health, is key to developing methods and policies for supporting these communities and managing them more efficiently. In this paper we investigate the prediction of community health based on the social behaviour exhibited by their members. We apply our analysis over 25 SAP online communities, and demonstrate the feasibility of using behaviour analysis to predict change in their health metrics. We show that accuracy of health prediction increases when using community-specific prediction models, rather than using a one-model-fits-all approach.
AB - Today's Web is social and largely driven by a wide variety of online communities. Many such communities are owned and managed by businesses that draw much value from these communities, in the form of efficient and cheaper customer support, generation of new ideas, fast spreading of information, etc. Understanding how to measure the health of online communities and how to predict its change over time, whether to better or to worse health, is key to developing methods and policies for supporting these communities and managing them more efficiently. In this paper we investigate the prediction of community health based on the social behaviour exhibited by their members. We apply our analysis over 25 SAP online communities, and demonstrate the feasibility of using behaviour analysis to predict change in their health metrics. We show that accuracy of health prediction increases when using community-specific prediction models, rather than using a one-model-fits-all approach.
U2 - 10.1109/SocialCom-PASSAT.2012.18
DO - 10.1109/SocialCom-PASSAT.2012.18
M3 - Conference contribution/Paper
SN - 978-1-4673-5638-1
SP - 267
EP - 276
BT - Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Confernece on Social Computing (SocialCom)
PB - IEEE
Y2 - 3 September 2012
ER -