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CIKM
2007
Springer

Improving the classification of newsgroup messages through social network analysis

9 years 21 days ago
Improving the classification of newsgroup messages through social network analysis
Newsgroup participants interact with their communities through conversation threads. They may respond to a message to answer a question, debate a topic, support or disagree with another person’s point, or digress and write about a different subject. Understanding the structure of threads and the sentiment of the participants’ interaction is valuable for search and moderation of newsgroups. 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 messa...
Blaz Fortuna, Eduarda Mendes Rodrigues, Natasa Mil
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where CIKM
Authors Blaz Fortuna, Eduarda Mendes Rodrigues, Natasa Milic-Frayling
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