A Survey on Stance Detection for Mis- and Disinformation Identification
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Dokumenter
- Fulltext
Forlagets udgivne version, 474 KB, PDF-dokument
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.
Originalsprog | Engelsk |
---|---|
Titel | Findings of the Association for Computational Linguistics : NAACL 2022 - Findings |
Forlag | Association for Computational Linguistics (ACL) |
Publikationsdato | 2022 |
Sider | 1259-1277 |
ISBN (Elektronisk) | 9781955917766 |
DOI | |
Status | Udgivet - 2022 |
Begivenhed | 2022 Findings of the Association for Computational Linguistics: NAACL 2022 - Seattle, USA Varighed: 10 jul. 2022 → 15 jul. 2022 |
Konference
Konference | 2022 Findings of the Association for Computational Linguistics: NAACL 2022 |
---|---|
Land | USA |
By | Seattle |
Periode | 10/07/2022 → 15/07/2022 |
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.
ID: 339345018