@inproceedings{jiang-etal-2023-categorising,
title = "Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of the {COVID}-19 Infodemic",
author = "Jiang, Ye and
Song, Xingyi and
Scarton, Carolina and
Singh, Iknoor and
Aker, Ahmet and
Bontcheva, Kalina",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ranlp-1.61",
pages = "556--567",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of the COVID-19 Infodemic
%A Jiang, Ye
%A Song, Xingyi
%A Scarton, Carolina
%A Singh, Iknoor
%A Aker, Ahmet
%A Bontcheva, Kalina
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F jiang-etal-2023-categorising
%X 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.
%U https://aclanthology.org/2023.ranlp-1.61
%P 556-567
Markdown (Informal)
[Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of the COVID-19 Infodemic](https://aclanthology.org/2023.ranlp-1.61) (Jiang et al., RANLP 2023)
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.