A Survey on Stance Detection for Mis- and Disinformation Identification
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Standard
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
APA
Vancouver
Author
Bibtex
}
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