Learning policies for sequential time and cost sensitive classification
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Proceedings of the 2007 international ACM conference on Supporting group work
Learning to recognize valuable tags
Proceedings of the 14th international conference on Intelligent user interfaces
Predicting community preference of comments on the Social Web
Web Intelligence and Agent Systems
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Online discussion forums are a valuable resource for people looking to find information, discuss ideas, and get advice on the Internet. Unfortunately, many forums have too much activity and information available, resulting in information overload. Moderation systems are implemented in some forums as a way to handle this problem, but due to sparsity issues, they are often not sufficient. In this paper we describe a novel method for learning from past moderations to develop a classifier that can perform automated moderation and thus address the sparsity problem. Additionally, we discuss the possibility of training a moderating classifieron a moderated forum and then applying it to an otherwise unmoderated forum.