@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://aclanthology.org/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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Detection and Resolution of Rumors and Misinformation with NLP
%A Derczynski, Leon
%A Zubiaga, Arkaitz
%Y Specia, Lucia
%Y Beck, Daniel
%S Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona, Spain (Online)
%F derczynski-zubiaga-2020-detection
%X 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.
%R 10.18653/v1/2020.coling-tutorials.4
%U https://aclanthology.org/2020.coling-tutorials.4/
%U https://doi.org/10.18653/v1/2020.coling-tutorials.4
%P 22-26
Markdown (Informal)
[Detection and Resolution of Rumors and Misinformation with NLP](https://aclanthology.org/2020.coling-tutorials.4/) (Derczynski & Zubiaga, COLING 2020)
ACL