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Learning from history: Predicting reverted work at the word level in wikipedia
Abstract Wikipedia's remarkable success in aggregatWikipedia's remarkable success in aggregating millions of contributions can pose a challenge for current editors, whose hard work may be reverted unless they understand and follow established norms, policies, and decisions and avoid contentious or proscribed terms. We present a machine learning model for predicting whether a contribution will be reverted based on word level features. Unlike previous models relying on editor-level characteristics, our model can make accurate predictions based only on the words a contribution changes. A key advantage of the model is that it can provide feedback on not only whether a contribution is likely to be rejected, but also the particular words that are likely to be controversial, enabling new forms of intelligent interfaces and visualizations. We examine the performance of the model across a variety of Wikipedia articles.el across a variety of Wikipedia articles.
Abstractsub Wikipedia's remarkable success in aggregatWikipedia's remarkable success in aggregating millions of contributions can pose a challenge for current editors, whose hard work may be reverted unless they understand and follow established norms, policies, and decisions and avoid contentious or proscribed terms. We present a machine learning model for predicting whether a contribution will be reverted based on word level features. Unlike previous models relying on editor-level characteristics, our model can make accurate predictions based only on the words a contribution changes. A key advantage of the model is that it can provide feedback on not only whether a contribution is likely to be rejected, but also the particular words that are likely to be controversial, enabling new forms of intelligent interfaces and visualizations. We examine the performance of the model across a variety of Wikipedia articles.el across a variety of Wikipedia articles.
Bibtextype inproceedings  +
Doi 10.1145/2145204.2145272  +
Has author Jeffrey Rzeszotarski + , Aniket Kittur +
Has extra keyword Accurate prediction + , Hard work + , Intelligent interface + , Machine learning + , New forms + , Reverted work + , Wikipedia + , Word level + , Computer-Supported Cooperative Work + , Interactive computer systems + , Learning systems + , Visualisation + , Websites +
Has keyword Applied machine learning + , Reverted work + , Wikipedia +
Isbn 9781450310864  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 437–440  +
Title Learning from history: Predicting reverted work at the word level in wikipedia +
Type conference paper  +
Year 2012 +
Creation dateThis property is a special property in this wiki. 7 November 2014 21:27:40  +
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 21:27:40  +
DateThis property is a special property in this wiki. 2012  +
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Learning from history: Predicting reverted work at the word level in wikipedia + Title
 

 

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