Online Communities: Designing Usability and Supporting Socialbilty
Online Communities: Designing Usability and Supporting Socialbilty
Invisible participants: how cultural capital relates to lurking behavior
Proceedings of the 15th international conference on World Wide Web
Preferential behavior in online groups
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Identifying user behavior in online social networks
Proceedings of the 1st Workshop on Social Network Systems
Developing the role concept for computer-supported collaborative learning: An explorative synthesis
Computers in Human Behavior
Role defining using behavior-based clustering in telecommunication network
Expert Systems with Applications: An International Journal
Towards semantically-interlinked online communities
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
Semi-automatic semantic moderation of web annotations
Proceedings of the 21st international conference companion on World Wide Web
A knowledge-based approach to augment applications with interaction traces
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
Ontology paper: Community analysis through semantic rules and role composition derivation
Web Semantics: Science, Services and Agents on the World Wide Web
Engaging Politicians with Citizens on Social Networking Sites: The WeGov Toolbox
International Journal of Electronic Government Research
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Understanding and forecasting the health of an online community is of great value to its owners and managers who have vested interests in its longevity and success. Nevertheless, the association between community evolution and the behavioural patterns and trends of its members is not clearly understood, which hinders our ability of making accurate predictions of whether a community is flourishing or diminishing. In this paper we use statistical analysis, combined with a semantic model and rules for representing and computing behaviour in online communities. We apply this model on a number of forum communities from Boards.ie to categorise behaviour of community members over time, and report on how different behaviour compositions correlate with positive and negative community growth in these forums.