Elusive vandalism detection in Wikipedia: A text stability-based approach
|Elusive vandalism detection in Wikipedia: A text stability-based approach|
|Author(s)||Wu Q., Irani D., Pu C., Ramaswamy L.|
|Published in||International Conference on Information and Knowledge Management, Proceedings|
|Keyword(s)||Classification, Vandalism detection, Wiki (Extra: Classification, Detection technique, Machine learning algorithms, Machine-learning, Vandalism detection, Wiki, Wikipedia, Knowledge management, Stability, Learning algorithms)|
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Elusive vandalism detection in Wikipedia: A text stability-based approach is a 2010 conference paper written in English by Wu Q., Irani D., Pu C., Ramaswamy L. and published in International Conference on Information and Knowledge Management, Proceedings.
The 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.
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