@InProceedings{yang-EtAl:2017:EMNLP20174,
  author    = {Yang, Diyi  and  Halfaker, Aaron  and  Kraut, Robert  and  Hovy, Eduard},
  title     = {Identifying Semantic Edit Intentions from Revisions in Wikipedia},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2000--2010},
  abstract  = {Most studies on human editing focus merely on syntactic revision operations,
	failing to capture the intentions behind revision changes, which are essential
	for facilitating the single and collaborative writing process. 
	In this work, we develop in collaboration with Wikipedia editors a 13-category
	taxonomy of the semantic intention behind edits in Wikipedia articles. Using
	labeled article edits, we build a computational classifier of intentions that
	achieved a micro-averaged F1 score of 0.621. We use this model to investigate
	edit intention effectiveness: how different types of edits predict the
	retention of newcomers and changes in the quality of articles, two key concerns
	for Wikipedia today. Our analysis shows that the types of edits that users make
	in their first session predict their subsequent survival as Wikipedia editors,
	and articles in different stages need different types of edits.},
  url       = {https://www.aclweb.org/anthology/D17-1213}
}