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Exploring Multiple Dynamic Social Networks in Computer-Mediated Communications: An Experimentally Validated Ecosystem
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Author(s): O. Isaac Osesina (Aware Inc., USA), M. Eduard Tudoreanu (University of Arkansas at Little Rock, USA), John P. McIntire (United States Air Force, USA), Paul R. Havig (United States Air Force, USA)and Eric E. Geiselman (United States Air Force, USA)
Copyright: 2019
Pages: 44
Source title:
Utilizing Big Data Paradigms for Business Intelligence
Source Author(s)/Editor(s): Jérôme Darmont (Université Lumière Lyon 2, France)and Sabine Loudcher (Université Lumière Lyon 2, France)
DOI: 10.4018/978-1-5225-4963-5.ch007
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Abstract
This chapter discusses concepts and tools for the exploration and visualization of computer-mediated communication (CMC), especially communication involving multiple users and taking place asynchronously. The work presented here is based on experimentally validated social networks (SN) extraction methods and consists of a diverse number of techniques for conveying the data to a business analyst. The chapter explores a large number of contexts ranging from direct social network graphs to more complex geographical, hierarchical, and conversation-centric approaches. User validation studies were conducted for the most representative techniques, centered both on extracting and on conveying of CMC data. The chapter examines methods for automatically extracting social networks, which is determining who is communicating with whom across different CMC channels. Beyond the network, the chapter focuses on the end-user discovery of topics and on integrating those with geographical, hierarchical, and user data. User-centric, interactive visualizations are presented from a functional perspective.
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