Citation
Amanda Bertsch and Steven Bethard. 2021. Detection of Puffery on the English Wikipedia. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 329–333, Online. Association for Computational Linguistics.Rights
Copyright © 2021 Association for Computational Linguistics. Licensed on a Creative Commons Attribution 4.0 International License.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
On Wikipedia, an online crowdsourced encyclopedia, volunteers enforce the encyclopedia’s editorial policies. Wikipedia’s policy on maintaining a neutral point of view has inspired recent research on bias detection, including “weasel words” and “hedges”. Yet to date, little work has been done on identifying “puffery,” phrases that are overly positive without a verifiable source. We demonstrate that collecting training data for this task requires some care, and construct a dataset by combining Wikipedia editorial annotations and information retrieval techniques. We compare several approaches to predicting puffery, and achieve 0.963 f1 score by incorporating citation features into a RoBERTa model. Finally, we demonstrate how to integrate our model with Wikipedia’s public infrastructure to give back to the Wikipedia editor community. © 2021 Association for Computational Linguistics.Note
Open access journalISBN
9781954085909Version
Final published versionae974a485f413a2113503eed53cd6c53
10.18653/v1/2021.wnut-1.36
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Except where otherwise noted, this item's license is described as Copyright © 2021 Association for Computational Linguistics. Licensed on a Creative Commons Attribution 4.0 International License.