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

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › 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.

Original language	English
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
Pages	22-26
ISBN (Electronic)	978-1-952148-30-9
DOIs	https://doi.org/10.18653/v1/2020.coling-tutorials.4
Publication status	Published - Dec 2020

Keywords

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

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

2020.coling-tutorials.4Final published version, 114 KBLicence: CC BY

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

Cite this

@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",

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. p. 22-26.

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

TY - GEN

T1 - Detection and Resolution of Rumors and Misinformation with NLP

AU - Derczynski, Leon

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

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

Keywords

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