Detection of Puffery on the English Wikipedia

Amanda Bertsch, Steven Bethard


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.
Anthology ID:
2021.wnut-1.36
Volume:
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
Month:
November
Year:
2021
Address:
Online
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
329–333
Language:
URL:
https://aclanthology.org/2021.wnut-1.36
DOI:
10.18653/v1/2021.wnut-1.36
Bibkey:
Cite (ACL):
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.
Cite (Informal):
Detection of Puffery on the English Wikipedia (Bertsch & Bethard, WNUT 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.wnut-1.36.pdf
Code
 abertsch72/wikipedia-puffery-detection