Learning from history: Predicting reverted work at the word level in wikipedia

From WikiPapers
Jump to: navigation, search

This appears to be a duplicate entry.

Publications with the same identifier or URL: Learning from history: Predicting reverted work at the word level in wikipedia, Learning from history: predicting reverted work at the word level in wikipedia.

Learning from history: Predicting reverted work at the word level in wikipedia is a 2012 conference paper written in English by Rzeszotarski J., Kittur A. and published in Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW.

[edit] Abstract

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. 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.

[edit] References

This section requires expansion. Please, help!

Cited by

Probably, this publication is cited by others, but there are no articles available for them in WikiPapers. Cited 3 time(s)