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Clustering on the Net: Applying an autoassociative neural network to computer-mediated discussions

Berthold, M.R., Sudweeks, F.ORCID: 0000-0003-0237-3671, Newton, S. and Coyne, R.D. (2006) Clustering on the Net: Applying an autoassociative neural network to computer-mediated discussions. Journal of Computer-Mediated Communication, 2 (4). JCMC2411.

Free to read: https://doi.org/10.1111/j.1083-6101.1997.tb00202.x
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Abstract

ProjectH, a research group of a hundred researchers, produced a huge amount of data from computer mediated discussions. The data classified several thousand postings from over 30 newsgroups into 46 categories. One approach to extract typical examples from this database is presented in this paper. An autoassociative neural network is trained on all 3000 coded messages and then used to construct typical messages under certain specified conditions. With this method the neural network can be used to create “typical” messages for several scenarios. This paper illustrates the architecture of the neural network that was used and explains the necessary modifications to the coding scheme. In addition several “typicality sets” produced by the neural net are shown and their generation is explained. In conclusion, the autoassociative neural network is used to explore threads and the types of messages that typically initiate or contribute longer lasting threads.

Item Type: Journal Article
Publisher: Oxford University Press
Copyright: © 1997 International Communication Association
URI: http://researchrepository.murdoch.edu.au/id/eprint/53148
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