Analyzing community knowledge sharing behavior

Abstract

The effectiveness of personalized support provided to virtual communities depends on what we know about a particular community and in which areas the community may need support. Following organizational psychology theories, we have developed algorithms to automatically detect patterns of knowledge sharing in a closely-knit virtual community, focusing on transactive memory, shared mental models, and cognitive centrality. The automatic detection of problematic areas enables taking decisions about notifications targeted at different community members but aiming at improving the functioning of the community as a whole. The paper presents graph-based algorithms for detecting community knowledge sharing patterns, and illustrates, based on a study with an existing community, how these patterns can be used for community-tailored support. © 2010 Springer-Verlag.