@inproceedings{bertsch-bethard-2021-detection,
    title = "Detection of Puffery on the {E}nglish {W}ikipedia",
    author = "Bertsch, Amanda  and
      Bethard, Steven",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)",
    month = nov,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.wnut-1.36/",
    doi = "10.18653/v1/2021.wnut-1.36",
    pages = "329--333",
    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 {\textquotedblleft}weasel words{\textquotedblright} and {\textquotedblleft}hedges{\textquotedblright}. Yet to date, little work has been done on identifying {\textquotedblleft}puffery,{\textquotedblright} 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."
}