@inproceedings{hosseinia-etal-2020-stance,
    title = "Stance Prediction for Contemporary Issues: Data and Experiments",
    author = "Hosseinia, Marjan  and
      Dragut, Eduard  and
      Mukherjee, Arjun",
    editor = "Ku, Lun-Wei  and
      Li, Cheng-Te",
    booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.socialnlp-1.5/",
    doi = "10.18653/v1/2020.socialnlp-1.5",
    pages = "32--40",
    abstract = "We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part of this work, we create a novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by procon.org in nonpartisan format. Experimental results show that a shallow recurrent neural network with sentiment or emotion information can reach competitive results compared to fine-tuned BERT with 20x fewer parameters. We also use a simple approach that explains which input phrases contribute to stance detection."
}