@inproceedings{mayfield-black-2019-stance,
    title = "Stance Classification, Outcome Prediction, and Impact Assessment: {NLP} Tasks for Studying Group Decision-Making",
    author = "Mayfield, Elijah  and
      Black, Alan",
    editor = "Volkova, Svitlana  and
      Jurgens, David  and
      Hovy, Dirk  and
      Bamman, David  and
      Tsur, Oren",
    booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-2108/",
    doi = "10.18653/v1/W19-2108",
    pages = "65--77",
    abstract = "In group decision-making, the nuanced process of conflict and resolution that leads to consensus formation is closely tied to the quality of decisions made. Behavioral scientists rarely have rich access to process variables, though, as unstructured discussion transcripts are difficult to analyze. Here, we define ways for NLP researchers to contribute to the study of groups and teams. We introduce three tasks alongside a large new corpus of over 400,000 group debates on Wikipedia. We describe the tasks and their importance, then provide baselines showing that BERT contextualized word embeddings consistently outperform other language representations."
}