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
- Cite (ACL):
- Leon Derczynski and Arkaitz Zubiaga. 2020. Detection and Resolution of Rumors and Misinformation with NLP. In Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts, pages 22–26, Barcelona, Spain (Online). International Committee for Computational Linguistics.
- Cite (Informal):
- Detection and Resolution of Rumors and Misinformation with NLP (Derczynski & Zubiaga, COLING 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.coling-tutorials.4.pdf