Detection and Resolution of Rumors and Misinformation with NLP

Leon Derczynski; Arkaitz Zubiaga

doi:10.18653/v1/2020.coling-tutorials.4

Detection and Resolution of Rumors and Misinformation with NLP

Leon Derczynski
, Arkaitz Zubiaga

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

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.

Originalsprog	Engelsk
Titel	Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts
Udgivelsessted	Barcelona, Spain (Online)
Forlag	Association for Computational Linguistics
Publikationsdato	dec. 2020
Sider	22-26
ISBN (Elektronisk)	978-1-952148-30-9
DOI	https://doi.org/10.18653/v1/2020.coling-tutorials.4
Status	Udgivet - dec. 2020
Begivenhed	International Conference on Computational Linguistics - Barcelona, Spanien Varighed: 8 dec. 2020 → 13 dec. 2020 Konferencens nummer: 28th

Konference

Konference	International Conference on Computational Linguistics
Nummer	28th
Land/Område	Spanien
By	Barcelona
Periode	08/12/2020 → 13/12/2020

Emneord

Misinformation Detection
Natural Language Processing
Claim Verification
Rumor Analysis
Deceptive Language Detection

Adgang til dokumentet

10.18653/v1/2020.coling-tutorials.4Licens: CC BY

2020.coling-tutorials.4Forlagets udgivne version, 114 KBLicens: CC BY

https://www.aclweb.org/anthology/2020.coling-tutorials.4.pdfLicens: CC BY

Citationsformater

@inproceedings{bfbf7f68f45d48758e1e99e6f8584aca,

title = "Detection and Resolution of Rumors and Misinformation with NLP",

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.",

keywords = "Misinformation Detection, Natural Language Processing, Claim Verification, Rumor Analysis, Deceptive Language Detection, Misinformation Detection, Natural Language Processing, Claim Verification, Rumor Analysis, Deceptive Language Detection",

author = "Leon Derczynski and Arkaitz Zubiaga",

year = "2020",

month = dec,

doi = "10.18653/v1/2020.coling-tutorials.4",

language = "English",

pages = "22--26",

booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts",

publisher = "Association for Computational Linguistics",

address = "United States",

note = "International Conference on Computational Linguistics ; Conference date: 08-12-2020 Through 13-12-2020",

}

Derczynski, L & Zubiaga, A 2020, Detection and Resolution of Rumors and Misinformation with NLP. i Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts. Association for Computational Linguistics, Barcelona, Spain (Online), s. 22-26, International Conference on Computational Linguistics, Barcelona, Spanien, 08/12/2020. https://doi.org/10.18653/v1/2020.coling-tutorials.4

Detection and Resolution of Rumors and Misinformation with NLP. / Derczynski, Leon; Zubiaga, Arkaitz.
Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts. Barcelona, Spain (Online): Association for Computational Linguistics, 2020. s. 22-26.

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

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AU - Zubiaga, Arkaitz

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Y1 - 2020/12

N2 - 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.

AB - 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.

KW - Misinformation Detection

KW - Natural Language Processing

KW - Claim Verification

KW - Rumor Analysis

KW - Deceptive Language Detection

KW - Misinformation Detection

KW - Natural Language Processing

KW - Claim Verification

KW - Rumor Analysis

KW - Deceptive Language Detection

U2 - 10.18653/v1/2020.coling-tutorials.4

DO - 10.18653/v1/2020.coling-tutorials.4

M3 - Article in proceedings

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BT - Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts

PB - Association for Computational Linguistics

CY - Barcelona, Spain (Online)

T2 - International Conference on Computational Linguistics

Y2 - 8 December 2020 through 13 December 2020

ER -

Detection and Resolution of Rumors and Misinformation with NLP

Abstract

Konference

Emneord

Adgang til dokumentet

Fingeraftryk

Citationsformater