Journal article
Clustering on the Net: Applying an autoassociative neural network to computer-mediated discussions
Journal of Computer-Mediated Communication, Vol.2(4), JCMC2411
2006
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.
Details
- Title
- Clustering on the Net: Applying an autoassociative neural network to computer-mediated discussions
- Authors/Creators
- M.R. Berthold (Author/Creator) - The University of SydneyF. Sudweeks (Author/Creator) - The University of SydneyS. Newton (Author/Creator) - Western Sydney UniversityR.D. Coyne (Author/Creator) - University of Edinburgh
- Publication Details
- Journal of Computer-Mediated Communication, Vol.2(4), JCMC2411
- Publisher
- Oxford University Press
- Identifiers
- 991005542449307891
- Copyright
- © 1997 International Communication Association
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
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