Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of the COVID-19 Infodemic
Ye Jiang, Xingyi Song, Carolina Scarton, Iknoor Singh, Ahmet Aker, Kalina Bontcheva
Abstract
The spread of COVID-19 misinformation on social media became a major challenge for citizens, with negative real-life consequences. Prior research focused on detection and/or analysis of COVID-19 misinformation. However, fine-grained classification of misinformation claims has been largely overlooked. The novel contribution of this paper is in introducing a new dataset which makes fine-grained distinctions between statements that assert, comment or question on false COVID-19 claims. This new dataset not only enables social behaviour analysis but also enables us to address both evidence-based and non-evidence-based misinformation classification tasks. Lastly, through leave claim out cross-validation, we demonstrate that classifier performance on unseen COVID-19 misinformation claims is significantly different, as compared to performance on topics present in the training data.- Anthology ID:
- 2023.ranlp-1.61
- Volume:
- Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
- Month:
- September
- Year:
- 2023
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd., Shoumen, Bulgaria
- Note:
- Pages:
- 556–567
- Language:
- URL:
- https://aclanthology.org/2023.ranlp-1.61
- DOI:
- Cite (ACL):
- Ye Jiang, Xingyi Song, Carolina Scarton, Iknoor Singh, Ahmet Aker, and Kalina Bontcheva. 2023. Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of the COVID-19 Infodemic. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 556–567, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
- Cite (Informal):
- Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of the COVID-19 Infodemic (Jiang et al., RANLP 2023)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2023.ranlp-1.61.pdf