Difference between revisions of "Learning from History: Predicting Reverted Work at the Word Level in Wikipedia"

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'''Learning from History: Predicting Reverted Work at the Word Level in Wikipedia''' - scientific work related to Wikipedia quality published in 2012, written by Jeffrey M. Rzeszotarski and Aniket Kittur.
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'''Learning from History: Predicting Reverted Work at the Word Level in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Jeffrey M. Rzeszotarski]] and [[Aniket Kittur]].
  
 
== Overview ==
 
== Overview ==
Wikipedia'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. Authors 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, 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. Authors examine the performance of the model across a variety of Wikipedia articles.
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Wikipedia'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. Authors 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, 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. Authors examine the performance of the model across a variety of [[Wikipedia]] articles.

Latest revision as of 05:44, 23 May 2020

Learning from History: Predicting Reverted Work at the Word Level in Wikipedia - scientific work related to Wikipedia quality published in 2012, written by Jeffrey M. Rzeszotarski and Aniket Kittur.

Overview

Wikipedia'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. Authors 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, 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. Authors examine the performance of the model across a variety of Wikipedia articles.