| Abstract
|
A very example of web 2.0 application is W … A very example of web 2.0 application is Wikipedia, an online encyclopedia where anyone can edit and share information. However, blatantly unproductive edits greatly undermine the quality of Wikipedia. Their irresponsible acts force editors to waste time undoing vandalisms. For the purpose of improving information quality on Wikipedia and freeing the maintainer from such repetitive tasks, machine learning methods have been proposed to detect vandalism automatically. However, most of them focused on mining new features which seem to be inexhaustible to be discovered. Therefore, the question of how to make the best use of these features needs to be tackled. In this paper, we leverage feature transformation techniques to analyze the features and propose a framework using these methods to enhance detection. Experiment results on the public dataset PAN-WVC-10 show that our method is effective and it provides another useful method to help detect vandalism in Wikipedia.hod to help detect vandalism in Wikipedia.
|
| Abstractsub
|
A very example of web 2.0 application is W … A very example of web 2.0 application is Wikipedia, an online encyclopedia where anyone can edit and share information. However, blatantly unproductive edits greatly undermine the quality of Wikipedia. Their irresponsible acts force editors to waste time undoing vandalisms. For the purpose of improving information quality on Wikipedia and freeing the maintainer from such repetitive tasks, machine learning methods have been proposed to detect vandalism automatically. However, most of them focused on mining new features which seem to be inexhaustible to be discovered. Therefore, the question of how to make the best use of these features needs to be tackled. In this paper, we leverage feature transformation techniques to analyze the features and propose a framework using these methods to enhance detection. Experiment results on the public dataset PAN-WVC-10 show that our method is effective and it provides another useful method to help detect vandalism in Wikipedia.hod to help detect vandalism in Wikipedia.
|
| Bibtextype
|
inproceedings +
|
| Doi
|
10.1007/978-3-642-35341-3_16 +
|
| Has author
|
Chang T. +
, Hong Lin +
, Yi-Sheng Lin +
|
| Has extra keyword
|
Feature transformations +
, Information quality +
, Machine learning methods +
, Online encyclopedia +
, PCA +
, Public dataset +
, Repetitive task +
, Vandalism +
, Web 2.0 applications +
, Wikipedia +
, Classification (of information) +
, Information retrieval +
, Infrared devices +
, Learning systems +
, Websites +
|
| Has keyword
|
Classification +
, PCA +
, Vandalism +
, Wikipedia +
|
| Isbn
|
9783642353406 +
|
| Language
|
English +
|
| Number of citations by publication
|
0 +
|
| Number of references by publication
|
0 +
|
| Pages
|
187–198 +
|
| Published in
|
Lecture Notes in Computer Science +
|
| Title
|
Feature transformation method enhanced vandalism detection in wikipedia +
|
| Type
|
conference paper +
|
| Volume
|
7675 LNCS +
|
| Year
|
2012 +
|
| Creation dateThis property is a special property in this wiki.
|
7 November 2014 19:28:05 +
|
| Categories |
Publications without license parameter +
, Publications without remote mirror parameter +
, Publications without archive mirror parameter +
, Publications without paywall mirror parameter +
, Conference papers +
, Publications without references parameter +
, Publications +
|
| Modification dateThis property is a special property in this wiki.
|
7 November 2014 19:28:05 +
|
|
|
|
| DateThis property is a special property in this wiki.
|
2012 +
|
