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    Automated Discovery and Analysis of Social Networks from Threaded Discussions

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    Author
    Gruzd, Anatoliy A
    Haythornthwaite, Caroline
    Issue Date
    2008
    Submitted date
    2008-02-14
    Keywords
    Information Science
    Distributed Learning
    Natural Language Processing
    Research Methods
    Computational Linguistics
    Learning Science
    Local subject classification
    Social networks
    Named entity recognition
    Natural language processing
    Collaborative learning
    Threaded discussions
    Methodology
    
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    Citation
    Automated Discovery and Analysis of Social Networks from Threaded Discussions 2008,
    URI
    http://hdl.handle.net/10150/105081
    Abstract
    To gain greater insight into the operation of online social networks, we applied Natural Language Processing (NLP) techniques to text-based communication to identify and describe underlying social structures in online communities. This paper presents our approach and preliminary evaluation for content-based, automated discovery of social networks. Our research question is: What syntactic and semantic features of postings in a threaded discussions help uncover explicit and implicit ties between network members, and which provide a reliable estimate of the strengths of interpersonal ties among the network members? To evaluate our automated procedures, we compare the results from the NLP processes with social networks built from basic who-to-whom data, and a sample of hand-coded data derived from a close reading of the text. For our test case, and as part of ongoing research on networked learning, we used the archive of threaded discussions collected over eight iterations of an online graduate class. We first associate personal names and nicknames mentioned in the postings with class participants. Next we analyze the context in which each name occurs in the postings to determine whether or not there is an interpersonal tie between a sender of the posting and a person mentioned in it. Because information exchange is a key factor in the operation and success of a learning community, we estimate and assign weights to the ties by measuring the amount of information exchanged between each pair of the nodes; information in this case is operationalized as counts of important concept terms in the postings as derived through the NLP analyses. Finally, we compare the resulting network(s) against those derived from other means, including basic who-to-whom data derived from posting sequences (e.g., whose postings follow whose). In this comparison we evaluate what is gained in understanding network processes by our more elaborate analyses.
    Type
    Conference Paper
    Language
    en
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