Automated Discovery and Analysis of Social Networks from Threaded Discussions

Persistent Link:
http://hdl.handle.net/10150/105081
Title:
Automated Discovery and Analysis of Social Networks from Threaded Discussions
Author:
Gruzd, Anatoliy A; Haythornthwaite, Caroline
Citation:
Automated Discovery and Analysis of Social Networks from Threaded Discussions 2008,
Issue Date:
2008
URI:
http://hdl.handle.net/10150/105081
Submitted date:
2008-02-14
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
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

Full metadata record

DC FieldValue Language
dc.contributor.authorGruzd, Anatoliy Aen_US
dc.contributor.authorHaythornthwaite, Carolineen_US
dc.date.accessioned2008-02-14T00:00:01Z-
dc.date.available2010-06-18T23:18:57Z-
dc.date.issued2008en_US
dc.date.submitted2008-02-14en_US
dc.identifier.citationAutomated Discovery and Analysis of Social Networks from Threaded Discussions 2008,en_US
dc.identifier.urihttp://hdl.handle.net/10150/105081-
dc.description.abstractTo 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.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.subjectInformation Scienceen_US
dc.subjectDistributed Learningen_US
dc.subjectNatural Language Processingen_US
dc.subjectResearch Methodsen_US
dc.subjectComputational Linguisticsen_US
dc.subjectLearning Scienceen_US
dc.subject.otherSocial networksen_US
dc.subject.otherNamed entity recognitionen_US
dc.subject.otherNatural language processingen_US
dc.subject.otherCollaborative learningen_US
dc.subject.otherThreaded discussionsen_US
dc.subject.otherMethodologyen_US
dc.titleAutomated Discovery and Analysis of Social Networks from Threaded Discussionsen_US
dc.typeConference Paperen_US
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