Detection and Resolution of Rumors and Misinformation with NLP

Leon Derczynski, Arkaitz Zubiaga


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.
Anthology ID:
2020.coling-tutorials.4
Volume:
Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
22–26
Language:
URL:
https://aclanthology.org/2020.coling-tutorials.4
DOI:
10.18653/v1/2020.coling-tutorials.4
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-tutorials.4.pdf