Towards a Social Network Intelligence Tool for visual Analysis of Virtual Communication Networks

• Main Author: Trier, Matthias
• Other Authors: Technische Universität Dresden, Fakultät Informatik
• Format: Others
• Language: English
• Published: Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden 2014
• Subjects: Konferenz; GeNeMe 2006; Neue Medien; virtuelle Kommunikation; soziales Netzwerk; conference; new media; Virtual Communication Networks; Social Network; ddc:330; rvk:QR 760
• Online Access: http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-140161; http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-140161; http://www.qucosa.de/fileadmin/data/qucosa/documents/14016/GeNeMe2006_331-342.pdf

Communities of Practice regularly utilize virtual means of communication. The according software support provides its members with many sophisticated features for generating content and for communicating with each other via the internet or intranet. However, functionalities to monitor, assess, coordinate, and communicate the quality and development of the underlying electronic networks of experts are frequently missing. To meet this need of increased manageability, this contribution introduces a Social Network Intelligence software approach which aims at supporting the comprehension of the structure and value of electronic communities by automatically extracting and mining available electronic data of various types of virtual communication networks, like e-mail archives, discussion groups, or instant messaging communication. Experimental structural visualizations employing Social Network Analysis methods are combined with Keyword Extraction to move towards a Social Network Intelligence approach which generates transparency of complex virtual communication networks. Together with a comprehensive visualization method, an approach for software-supported communication network measurement and evaluation is suggested. It supports the identification of important participants, topics, or clusters in the network, evaluates the interpersonal communication structure and visually traces the evolvement of the knowledge exchange over time.