@inproceedings{kolhatkar-taboada-2017-using,
    title = "Using {N}ew {Y}ork {T}imes Picks to Identify Constructive Comments",
    author = "Kolhatkar, Varada  and
      Taboada, Maite",
    editor = "Popescu, Octavian  and
      Strapparava, Carlo",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/W17-4218/",
    doi = "10.18653/v1/W17-4218",
    pages = "100--105",
    abstract = "We examine the extent to which we are able to automatically identify constructive online comments. We build several classifiers using New York Times Picks as positive examples and non-constructive thread comments from the Yahoo News Annotated Comments Corpus as negative examples of constructive online comments. We evaluate these classifiers on a crowd-annotated corpus containing 1,121 comments. Our best classifier achieves a top F1 score of 0.84."
}