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.
|Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts
|Barcelona, Spain (Online)
|Association for Computational Linguistics
|Udgivet - dec. 2020