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

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

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",

}

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

TY - GEN

T1 - Detection and Resolution of Rumors and Misinformation with NLP

AU - Derczynski, Leon

AU - Zubiaga, Arkaitz

PY - 2020/12

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

SP - 22

EP - 26

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

PB - Association for Computational Linguistics

CY - Barcelona, Spain (Online)

ER -

Detection and Resolution of Rumors and Misinformation with NLP

Abstract

Emneord

Adgang til dokumentet

Fingeraftryk

Citationsformater