Rowe, Matthew and Alani, Harith
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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.
|Item Type:||Conference Item|
|Copyright Holders:||2012 Not known|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Kay Dave|
|Date Deposited:||21 Aug 2012 09:47|
|Last Modified:||05 Aug 2016 18:32|
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