Learning from history: Predicting reverted work at the word level in wikipedia
|Learning from history: Predicting reverted work at the word level in wikipedia|
|Author(s)||Rzeszotarski J., Kittur A.|
|Published in||Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW|
|Keyword(s)||applied machine learning, reverted work, wikipedia (Extra: Accurate prediction, Hard work, Intelligent interface, Machine-learning, New forms, reverted work, Wikipedia, Word level, Computer supported cooperative work, Interactive computer systems, Learning systems, Visualization, Websites)|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of conference papers|
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.
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.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers. Cited 3 time(s)