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

@inproceedings{Rzeszotarski2012LearningFH,
  title={Learning from history: predicting reverted work at the word level in wikipedia},
  author={Jeffrey M. Rzeszotarski and Aniket Kittur},
  booktitle={CSCW},
  year={2012}
}
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… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.

Similar Papers

Loading similar papers…