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Argument Mapping and Content Fusion to Support the Analysis and Synthesis of Information in Online Discussions

Argument Mapping and Content Fusion to Support the Analysis and Synthesis of Information in Online Discussions

Ali Gürkan, Luca Iandoli
Copyright: © 2014 |Volume: 6 |Issue: 1 |Pages: 20
ISSN: 1941-6296|EISSN: 1941-630X|EISBN13: 9781466653634|DOI: 10.4018/ijdsst.2014010102
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MLA

Gürkan, Ali, and Luca Iandoli. "Argument Mapping and Content Fusion to Support the Analysis and Synthesis of Information in Online Discussions." IJDSST vol.6, no.1 2014: pp.14-33. http://doi.org/10.4018/ijdsst.2014010102

APA

Gürkan, A. & Iandoli, L. (2014). Argument Mapping and Content Fusion to Support the Analysis and Synthesis of Information in Online Discussions. International Journal of Decision Support System Technology (IJDSST), 6(1), 14-33. http://doi.org/10.4018/ijdsst.2014010102

Chicago

Gürkan, Ali, and Luca Iandoli. "Argument Mapping and Content Fusion to Support the Analysis and Synthesis of Information in Online Discussions," International Journal of Decision Support System Technology (IJDSST) 6, no.1: 14-33. http://doi.org/10.4018/ijdsst.2014010102

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Abstract

While online conversations are very popular, the content generated by participants is very often overwhelming, poorly organized and often of questionable quality. In this article we use two methods, a text analysis technique, Vector Space Modeling (VSM) and clustering to create a methodology to organize and aggregate information generated by users using Online collaborative Argumentation (OA) in their online debate. An alternative to other widely used conversational tools such as online forums, OA is supposed to help users to join their efforts to construct a shared knowledge representation in the form of an argument map in which multiple points of view can coexist and be presented in the form of a well-organized knowledge object. To see whether this supposition comes into effect we first apply VSM to summarize argument map content as a document space and then use clustering to transform it to a limited number of higher order semantic categories. We apply the methodology to more than 3000 posts created in an online debate of about 160 participants using an online argumentation platform and we show how this methodology can be used to effectively organize and evaluate content generated by a large number of users in online discussions.

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