@inproceedings{rethmeier-etal-2018-learning,
    title = "Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task {CNN}s",
    author = {Rethmeier, Nils  and
      H{\"u}bner, Marc  and
      Hennig, Leonhard},
    editor = "Balahur, Alexandra  and
      Mohammad, Saif M.  and
      Hoste, Veronique  and
      Klinger, Roman",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-6246/",
    doi = "10.18653/v1/W18-6246",
    pages = "316--321",
    abstract = "Comments on web news contain controversies that manifest as inter-group agreement-conflicts. Tracking such \textit{rapidly evolving controversy} could ease conflict resolution or journalist-user interaction. However, this presupposes controversy online-prediction that scales to diverse domains using incidental supervision signals instead of manual labeling. To more deeply interpret comment-controversy model decisions we frame prediction as binary classification and evaluate baselines and multi-task CNNs that use an auxiliary news-genre-encoder. Finally, we use ablation and interpretability methods to determine the impacts of topic, discourse and sentiment indicators, contextual vs. global word influence, as well as genre-keywords vs. per-genre-controversy keywords {--} to find that the models learn plausible controversy features using only incidentally supervised signals."
}