A Survey on Stance Detection for Mis- and Disinformation Identification

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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A Survey on Stance Detection for Mis- and Disinformation Identification. / Hardalov, Momchil; Arora, Arnav; Nakov, Preslav; Augenstein, Isabelle.

Findings of the Association for Computational Linguistics: NAACL 2022 - Findings. Association for Computational Linguistics (ACL), 2022. p. 1259-1277.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Hardalov, M, Arora, A, Nakov, P & Augenstein, I 2022, A Survey on Stance Detection for Mis- and Disinformation Identification. in Findings of the Association for Computational Linguistics: NAACL 2022 - Findings. Association for Computational Linguistics (ACL), pp. 1259-1277, 2022 Findings of the Association for Computational Linguistics: NAACL 2022, Seattle, United States, 10/07/2022. https://doi.org/10.18653/v1/2022.findings-naacl.94

APA

Hardalov, M., Arora, A., Nakov, P., & Augenstein, I. (2022). A Survey on Stance Detection for Mis- and Disinformation Identification. In Findings of the Association for Computational Linguistics: NAACL 2022 - Findings (pp. 1259-1277). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.findings-naacl.94

Vancouver

Hardalov M, Arora A, Nakov P, Augenstein I. A Survey on Stance Detection for Mis- and Disinformation Identification. In Findings of the Association for Computational Linguistics: NAACL 2022 - Findings. Association for Computational Linguistics (ACL). 2022. p. 1259-1277 https://doi.org/10.18653/v1/2022.findings-naacl.94

Author

Hardalov, Momchil ; Arora, Arnav ; Nakov, Preslav ; Augenstein, Isabelle. / A Survey on Stance Detection for Mis- and Disinformation Identification. Findings of the Association for Computational Linguistics: NAACL 2022 - Findings. Association for Computational Linguistics (ACL), 2022. pp. 1259-1277

Bibtex

@inproceedings{c46bd97ef19d420698e35feb992b4f19,
title = "A Survey on Stance Detection for Mis- and Disinformation Identification",
abstract = "Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information). Stance detection has been framed in different ways, including (a) as a component of fact-checking, rumour detection, and detecting previously fact-checked claims, or (b) as a task in its own right. While there have been prior efforts to contrast stance detection with other related tasks such as argumentation mining and sentiment analysis, there is no existing survey on examining the relationship between stance detection and mis- and disinformation detection. Here, we aim to bridge this gap by reviewing and analysing existing work in this area, with mis- and disinformation in focus, and discussing lessons learnt and future challenges.",
author = "Momchil Hardalov and Arnav Arora and Preslav Nakov and Isabelle Augenstein",
note = "Publisher Copyright: {\textcopyright} Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.; 2022 Findings of the Association for Computational Linguistics: NAACL 2022 ; Conference date: 10-07-2022 Through 15-07-2022",
year = "2022",
doi = "10.18653/v1/2022.findings-naacl.94",
language = "English",
pages = "1259--1277",
booktitle = "Findings of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
address = "United States",

}

RIS

TY - GEN

T1 - A Survey on Stance Detection for Mis- and Disinformation Identification

AU - Hardalov, Momchil

AU - Arora, Arnav

AU - Nakov, Preslav

AU - Augenstein, Isabelle

N1 - Publisher Copyright: © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

PY - 2022

Y1 - 2022

N2 - Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information). Stance detection has been framed in different ways, including (a) as a component of fact-checking, rumour detection, and detecting previously fact-checked claims, or (b) as a task in its own right. While there have been prior efforts to contrast stance detection with other related tasks such as argumentation mining and sentiment analysis, there is no existing survey on examining the relationship between stance detection and mis- and disinformation detection. Here, we aim to bridge this gap by reviewing and analysing existing work in this area, with mis- and disinformation in focus, and discussing lessons learnt and future challenges.

AB - Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information). Stance detection has been framed in different ways, including (a) as a component of fact-checking, rumour detection, and detecting previously fact-checked claims, or (b) as a task in its own right. While there have been prior efforts to contrast stance detection with other related tasks such as argumentation mining and sentiment analysis, there is no existing survey on examining the relationship between stance detection and mis- and disinformation detection. Here, we aim to bridge this gap by reviewing and analysing existing work in this area, with mis- and disinformation in focus, and discussing lessons learnt and future challenges.

UR - http://www.scopus.com/inward/record.url?scp=85136962653&partnerID=8YFLogxK

U2 - 10.18653/v1/2022.findings-naacl.94

DO - 10.18653/v1/2022.findings-naacl.94

M3 - Article in proceedings

AN - SCOPUS:85136962653

SP - 1259

EP - 1277

BT - Findings of the Association for Computational Linguistics

PB - Association for Computational Linguistics (ACL)

T2 - 2022 Findings of the Association for Computational Linguistics: NAACL 2022

Y2 - 10 July 2022 through 15 July 2022

ER -

ID: 339345018