Enhancing Social Network Analysis with a Concept-Based Text Mining Approach to Discover Key Members on a Virtual Community of Practice
In order to have a successful VCoP two important tasks must be performed: on the one hand, it is always important that community provide useful information to every member by a good organization of contents and topics; on the other hand, to understand the behavior of members (i.e. which are the key members or experts, discover communities, etc). Social Network Analysis (SNA) is a powerful tool to understand the communities’ members, however, our theses is that state-of-the-art in SNA it is not sufficient to obtain useful knowledge from a VCoP. Moreover, we think that traditional SNA may lead to discover wrong results. We propose to combine traditional SNA with data mining techniques in order to produce results closer to reality and gather useful knowledge for VCoPs’ enhancement. In this work, we focused in discovering key members on a VCoP combining SNA with concept-based text mining.We successfully tested our approach on a real VCoP with more than 2500 members and we validate our results asking the community administrators.
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