A Survey on Stance Detection for Mis- and Disinformation Identification
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Documents
- Fulltext
Final published version, 474 KB, PDF document
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.
Original language | English |
---|---|
Title of host publication | Findings of the Association for Computational Linguistics : NAACL 2022 - Findings |
Publisher | Association for Computational Linguistics (ACL) |
Publication date | 2022 |
Pages | 1259-1277 |
ISBN (Electronic) | 9781955917766 |
DOIs | |
Publication status | Published - 2022 |
Event | 2022 Findings of the Association for Computational Linguistics: NAACL 2022 - Seattle, United States Duration: 10 Jul 2022 → 15 Jul 2022 |
Conference
Conference | 2022 Findings of the Association for Computational Linguistics: NAACL 2022 |
---|---|
Land | United States |
By | Seattle |
Periode | 10/07/2022 → 15/07/2022 |
Bibliographical note
Publisher Copyright:
© Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.
Number of downloads are based on statistics from Google Scholar and www.ku.dk
ID: 339345018