A Survey on Stance Detection for Mis- and Disinformation Identification

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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.

OriginalsprogEngelsk
TitelFindings of the Association for Computational Linguistics : NAACL 2022 - Findings
ForlagAssociation for Computational Linguistics (ACL)
Publikationsdato2022
Sider1259-1277
ISBN (Elektronisk)9781955917766
DOI
StatusUdgivet - 2022
Begivenhed2022 Findings of the Association for Computational Linguistics: NAACL 2022 - Seattle, USA
Varighed: 10 jul. 202215 jul. 2022

Konference

Konference2022 Findings of the Association for Computational Linguistics: NAACL 2022
LandUSA
BySeattle
Periode10/07/202215/07/2022

Bibliografisk note

Funding Information:
We would like to thank the anonymous reviewers for their useful feedback. Isabelle Augenstein’s research is partially funded by a DFF Sapere Aude research leader grant with grant number 0171-00034B. The work is also part of the Tanbih megaproject, which is developed at the Qatar Computing Research Institute, HBKU, and aims to limit the impact of “fake news,” propaganda, and media bias by making users aware of what they are reading, thus promoting media literacy and critical thinking.

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

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