Detection and Resolution of Rumors and Misinformation with NLP

Leon Derczynski, Arkaitz Zubiaga

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts
Place of PublicationBarcelona, Spain (Online)
PublisherAssociation for Computational Linguistics
Publication dateDec 2020
ISBN (Electronic)978-1-952148-30-9
Publication statusPublished - Dec 2020


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