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Automatic vandalism detection in Wikipedia with active associative classification
Abstract Wikipedia and other free editing services Wikipedia and other free editing services for collaboratively generated content have quickly grown in popularity. However, the lack of editing control has made these services vulnerable to various types of malicious actions such as vandalism. State-of-the-art vandalism detection methods are based on supervised techniques, thus relying on the availability of large and representative training collections. Building such collections, often with the help of crowdsourcing, is very costly due to a natural skew towards very few vandalism examples in the available data as well as dynamic patterns. Aiming at reducing the cost of building such collections, we present a new active sampling technique coupled with an on-demand associative classification algorithm for Wikipedia vandalism detection. We show that our classifier enhanced with a simple undersampling technique for building the training set outperforms state-of-the-art classifiers such as SVMs and kNNs. Furthermore, by applying active sampling, we are able to reduce the need for training in almost 96% with only a small impact on detection results. only a small impact on detection results.
Abstractsub Wikipedia and other free editing services Wikipedia and other free editing services for collaboratively generated content have quickly grown in popularity. However, the lack of editing control has made these services vulnerable to various types of malicious actions such as vandalism. State-of-the-art vandalism detection methods are based on supervised techniques, thus relying on the availability of large and representative training collections. Building such collections, often with the help of crowdsourcing, is very costly due to a natural skew towards very few vandalism examples in the available data as well as dynamic patterns. Aiming at reducing the cost of building such collections, we present a new active sampling technique coupled with an on-demand associative classification algorithm for Wikipedia vandalism detection. We show that our classifier enhanced with a simple undersampling technique for building the training set outperforms state-of-the-art classifiers such as SVMs and kNNs. Furthermore, by applying active sampling, we are able to reduce the need for training in almost 96% with only a small impact on detection results. only a small impact on detection results.
Bibtextype inproceedings  +
Doi 10.1007/978-3-642-33290-6_15  +
Has author Maria Sumbana + , Goncalves M.A. + , Rodrigo Silva + , Jussara Almeida + , Adriano Veloso +
Has extra keyword Active sampling + , Associative classification + , Crowdsourcing + , Detection methods + , Dynamic patterns + , Training sets + , Under-sampling + , Wikipedia + , Classification (of information) + , Digital libraries + , Websites +
Has paywall mirror http://www.springerlink.com/content/n6980541705121m5/  +
Isbn 9783642332890  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 138–143  +
Peer-reviewed Yes  +
Published in Lecture Notes in Computer Science +
Title Automatic vandalism detection in Wikipedia with active associative classification +
Type conference paper  +
Volume 7489 LNCS  +
Year 2012 +
Creation dateThis property is a special property in this wiki. 7 November 2014 19:10:07  +
Categories Publications without keywords parameter  + , Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 22 November 2014 18:28:43  +
DateThis property is a special property in this wiki. 2012  +
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