Detecting Wikipedia vandalism with a contributing efficiency-based approach
|Detecting Wikipedia vandalism with a contributing efficiency-based approach|
|Author(s)||Tang X., Zhou G., Fu Y., Gan L., Yu W., Li S.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Classification, Vandalism detection, Wikipedia (Extra: Detection algorithm, Detection methods, Language features, Machine-learning, Online encyclopedia, Open content, Wikipedia, Classification (of information), Learning systems, Systems engineering, Websites, Efficiency)|
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Detecting Wikipedia vandalism with a contributing efficiency-based approach is a 2012 conference paper written in English by Tang X., Zhou G., Fu Y., Gan L., Yu W., Li S. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
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.
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