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Elusive vandalism detection in Wikipedia: A text stability-based approach
Abstract The open collaborative nature of wikis encThe open collaborative nature of wikis encourages participation of all users, but at the same time exposes their content to vandalism. The current vandalism-detection techniques, while effective against relatively obvious vandalism edits, prove to be inadequate in detecting increasingly prevalent sophisticated (or elusive) vandal edits. We identify a number of vandal edits that can take hours, even days, to correct and propose a text stability-based approach for detecting them. Our approach is focused on the likelihood of a certain part of an article being modified by a regular edit. In addition to text-stability, our machine learning-based technique also takes into account edit patterns. We evaluate the performance of our approach on a corpus comprising of 15000 manually labeled edits from the Wikipedia Vandalism PAN corpus. The experimental results show that text-stability is able to improve the performance of the selected machine-learning algorithms significantly.machine-learning algorithms significantly.
Abstractsub The open collaborative nature of wikis encThe open collaborative nature of wikis encourages participation of all users, but at the same time exposes their content to vandalism. The current vandalism-detection techniques, while effective against relatively obvious vandalism edits, prove to be inadequate in detecting increasingly prevalent sophisticated (or elusive) vandal edits. We identify a number of vandal edits that can take hours, even days, to correct and propose a text stability-based approach for detecting them. Our approach is focused on the likelihood of a certain part of an article being modified by a regular edit. In addition to text-stability, our machine learning-based technique also takes into account edit patterns. We evaluate the performance of our approach on a corpus comprising of 15000 manually labeled edits from the Wikipedia Vandalism PAN corpus. The experimental results show that text-stability is able to improve the performance of the selected machine-learning algorithms significantly.machine-learning algorithms significantly.
Bibtextype inproceedings  +
Doi 10.1145/1871437.1871732  +
Has author Wu Q. + , Danesh Irani + , Calton Pu + , Lakshmish Ramaswamy +
Has extra keyword Classification + , Detection technique + , Machine learning algorithms + , Machine learning + , Vandalism detection + , Wiki + , Wikipedia + , Knowledge management + , Stability + , Learning algorithms +
Has keyword Classification + , Vandalism detection + , Wiki +
Isbn 9781450300995  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 1797–1800  +
Published in International Conference on Information and Knowledge Management, Proceedings +
Title Elusive vandalism detection in Wikipedia: A text stability-based approach +
Type conference paper  +
Year 2010 +
Creation dateThis property is a special property in this wiki. 7 November 2014 13:51:42  +
Categories Duplicate publication  + , 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 13:51:42  +
DateThis property is a special property in this wiki. 2010  +
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Elusive vandalism detection in Wikipedia: A text stability-based approach + Title
 

 

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