@inproceedings{derczynski-zubiaga-2020-detection,
    title = "Detection and Resolution of Rumors and Misinformation with {NLP}",
    author = "Derczynski, Leon  and
      Zubiaga, Arkaitz",
    editor = "Specia, Lucia  and
      Beck, Daniel",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.coling-tutorials.4/",
    doi = "10.18653/v1/2020.coling-tutorials.4",
    pages = "22--26",
    abstract = "Detecting and grounding false and misleading claims on the web has grown to form a substantial sub-field of NLP. The sub-field addresses problems at multiple different levels of misinformation detection: identifying check-worthy claims; tracking claims and rumors; rumor collection and annotation; grounding claims against knowledge bases; using stance to verify claims; and applying style analysis to detect deception. This half-day tutorial presents the theory behind each of these steps as well as the state-of-the-art solutions."
}