Title :
Learning to perform moderation in online forums
Author :
Arnt, Andrew ; Zilberstein, Shlomo
Author_Institution :
Dept. of Comput. Sci., Massachusetts Univ., Amherst, MA, USA
Abstract :
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. 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 classifier on a moderated forum and then applying it to an otherwise unmoderated forum.
Keywords :
Internet; classification; information filters; information retrieval; learning (artificial intelligence); Internet; information overload; machine learning; moderated forum; moderating classifier; moderation systems; online discussion forums; sparsity issues; unmoderated forum; Accuracy; Aggregates; Collaboration; Computer science; Discussion forums; Filtering algorithms; Filters; Humans; Recommender systems; Search engines;
Conference_Titel :
Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on
Print_ISBN :
0-7695-1932-6
DOI :
10.1109/WI.2003.1241285