Detection and Resolution of Rumors and Misinformation with NLP
- ,
- Arkaitz Zubiaga
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewOpen access
Publication Information
Output type
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewOriginal language
EnglishPages from-to (Number of pages)
Pages 22-26Publication milestones
- Published - 12/2020
Publication status
Published - 12/2020
Place of publication
Barcelona, Spain (Online)Publisher
Association for Computational Linguistics, United StatesISBN (Electronic)
978-1-952148-30-9Publication IDs
- Scopus: 85129834343
Host publication title
Proceedings of the 28th International Conference on Computational Linguistics: Tutorial AbstractsAbstract
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.
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Citations
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Captures
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Access to documents
Final published version, 113.62 KB
License:CC BY, opens in new tab
Final published version
License:CC BY, opens in new tab
Related Event
Title
International Conference on Computational Linguistics
Event type
ConferenceDegree of recognition
International eventDate
08/12/2020 - 13/12/2020Location
BarcelonaSpain
