Detection and Resolution of Rumors and Misinformation with NLP
Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-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.
|Title of host publication||Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts|
|Place of Publication||Barcelona, Spain (Online)|
|Publisher||Association for Computational Linguistics|
|Publication date||Dec 2020|
|Publication status||Published - Dec 2020|