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EC-WEB '00 Proceedings of the First International Conference on Electronic Commerce and Web Technologies
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The VLDB Journal — The International Journal on Very Large Data Bases
Online communities: focusing on sociability and usability
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WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
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Communications of the ACM - Spam and the ongoing battle for the inbox
Proceedings of the 2007 ACM SIGMIS CPR conference on Computer personnel research: The global information technology workforce
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Data Mining and Knowledge Discovery
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Adaptive Web SitesA Knowledge Extraction from Web Data Approach
Proceedings of the 2008 conference on Adaptive Web Sites: A Knowledge Extraction from Web Data Approach
Virtual Communities of Practice's Purpose Evolution Analysis Using a Concept-Based Mining Approach
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Topic-based social network analysis for virtual communities of interests in the Dark Web
ACM SIGKDD Workshop on Intelligence and Security Informatics
Topic-based social network analysis for virtual communities of interests in the dark web
ACM SIGKDD Explorations Newsletter
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Web Intelligence and Agent Systems - Web Intelligence and Communities
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Social networks (SN) have sprouted on the Internet in a very quick way in the last few years. As a large quantity of users starts using them, a lot of social problems are starting to appear, and therefore these sites need to be moderated. However, the data and information volume are so large that it is impossible for a human administrator to handle many of the most common moderation tasks. Web Usage Mining is very useful for understanding user behavior on Websites, opening an opportunity for finding patterns, which can help with decisions afterwards. One of these techniques is clustering, which uses the notion of distance between two behaviors, and tries to capture it among special characteristics. Dissimilarity measures are constructed using important aspects of Website user behavior, but none commonly used ones, such as Cooley et al. distance [3]; help deal with social networking user behavior for moderation tasks. In this work a new dissimilarity measure is used combining usage and content's semantics while interacting with social network platform objects, such as actions, action content, and classification chosen by the user. The measure of this work was successfully tested in a virtual community of practice, obtaining an automatic classification for supporting moderation activities.