Identifying Semantic Edit Intentions from Revisions in Wikipedia

Diyi Yang, Aaron Halfaker, Robert Kraut, Eduard Hovy


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.
Anthology ID:
D17-1213
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2000–2010
Language:
URL:
https://www.aclweb.org/anthology/D17-1213
DOI:
10.18653/v1/D17-1213
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1213.pdf
Video:
 https://vimeo.com/238233635