Mining and Visualizing Research Networks using the Artefact-Actor-Network Approach

Wolfgang Reinhardt, Adrian Wilke, Matthias Moi, Hendrik Drachsler, Peter Sloep

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    Abstract

    Virtual communities are increasingly relying on technologies and tools of the so-called Web 2.0. In the context of scientific events and topical Research Networks, researchers use Social Media as one main communication channel. This raises the question, how to monitor and analyze such Research Networks. In this chapter we argue that Artefact-Actor-Networks (AANs) serve well for modeling, storing and mining the social interactions around digital learning resources originating from various learning services. In order to deepen the model of AANs and its application to Research Networks, a relevant theoretical background as well as clues for a prototypical reference implementation are provided. This is followed by the analysis of six Research Networks and a detailed inspection of the results. Moreover, selected networks are visualized. Research Networks of the same type show similar descriptive measures while different types are not directly comparable to each other. Further, our analysis shows that narrowness of a Research Network's subject area can be predicted using the connectedness of semantic similarity networks. Finally conclusions are drawn and implications for future research are discussed.
    Original languageEnglish
    Title of host publicationComputational Social Networks
    Subtitle of host publicationMining and Visualization
    EditorsAjith Abraham
    Place of PublicationLondon, UK
    PublisherSpringer
    Pages233-267
    Number of pages35
    Edition1
    ISBN (Electronic)978-1-4471-4054-2
    ISBN (Print)978-1-4471-4053-5, 978-1-4471-6237-7
    DOIs
    Publication statusPublished - 14 Jun 2012

    Keywords

    • knowledge work
    • knowledge worker
    • research networks
    • visualization
    • social network analysis
    • twitter
    • learning networks
    • research 2.0
    • social media
    • semantic similarity
    • community mining

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