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Improving the classification of newsgroup messages through social network analysis

Published: 06 November 2007 Publication History

Abstract

Improving the classification of newsgroup messages through social network analysis.
In this paper, we focus on automatic classification of message replies into several types. For representing messages we consider rich feature sets that combine the standard author reply-to network properties with features derived from four additional structures identified in the data: 1) a network of authors who participate in the same threads, 2) network of authors who post similar content, 3) network of threads sharing common authors, and 4) network of content-related threads.
For selected newsgroups we train linear SVM classifiers to identify agreement and disagreement with the original message, and question and answer patterns in the threads. We show that the use of newly defined features substantially improves classification of messages in comparison with the SVM model based only on the standard reply-to network.

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cover image ACM Conferences
CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
November 2007
1048 pages
ISBN:9781595938039
DOI:10.1145/1321440
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Published: 06 November 2007

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Author Tags

  1. communities
  2. message classification
  3. newsgroups
  4. social networks

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