ITU

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

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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

Harvard

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

APA

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

Vancouver

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

Author

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

Bibtex

@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.",
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",

}

RIS

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.

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 -

ID: 85640447