Detecting wikipedia vandalism with a contributing efficiency-based approach

Authors:
Xiaoyue Tang;Guofu Zhou;Yuchen Fu;Lin Gan;Wei Yu;Shijun Li
Affiliations:
State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China,School of Computer, Wuhan University, Wuhan, China;State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China;School of Computer Science & Technology, Soochow University, Suzhou, China;School of Computer, Wuhan University, Wuhan, China;School of Computer, Wuhan University, Wuhan, China;State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China,School of Computer, Wuhan University, Wuhan, China
Venue:
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Year:
2012

Citing 7
Cited 0

Creating, destroying, and restoring value in wikipedia

Proceedings of the 2007 international ACM conference on Supporting group work
Detecting Wikipedia vandalism via spatio-temporal analysis of revision metadata?

Proceedings of the Third European Workshop on System Security
Detecting Wikipedia vandalism with active learning and statistical language models

Proceedings of the 4th workshop on Information credibility
Automatic vandalism detection in Wikipedia

ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Assigning trust to Wikipedia content

WikiSym '08 Proceedings of the 4th International Symposium on Wikis
Elusive vandalism detection in wikipedia: a text stability-based approach

CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Wikipedia vandalism detection: combining natural language, metadata, and reputation features

CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

The collaborative nature of wiki has distinguished Wikipedia as an online encyclopedia but also makes the open contents vulnerable against vandalism. The current vandalism detection methods relying on basic statistic language features work well for explicitly offensive edits that perform massive changes. However, these techniques are evadable for the elusive vandal edits which make only a few unproductive or dishonest modifications. In this paper we proposed a contributing efficiency-based approach to detect the vandalism in Wikipedia and implement it with machine-learning based classifiers that incorporate the contributing efficiency along with other languages features. The results of extensional experiment show that the contributing efficiency can improve the recall of machine learning-based vandalism detection algorithms significantly.