Transformers to Fight the COVID-19 Infodemic

Lasitha Uyangodage, Tharindu Ranasinghe, Hansi Hettiarachchi


Abstract
The massive spread of false information on social media has become a global risk especially in a global pandemic situation like COVID-19. False information detection has thus become a surging research topic in recent months. NLP4IF-2021 shared task on fighting the COVID-19 infodemic has been organised to strengthen the research in false information detection where the participants are asked to predict seven different binary labels regarding false information in a tweet. The shared task has been organised in three languages; Arabic, Bulgarian and English. In this paper, we present our approach to tackle the task objective using transformers. Overall, our approach achieves a 0.707 mean F1 score in Arabic, 0.578 mean F1 score in Bulgarian and 0.864 mean F1 score in English ranking 4th place in all the languages.
Anthology ID:
2021.nlp4if-1.20
Volume:
Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
Month:
June
Year:
2021
Address:
Online
Venues:
NAACL | NLP4IF
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
130–135
Language:
URL:
https://aclanthology.org/2021.nlp4if-1.20
DOI:
10.18653/v1/2021.nlp4if-1.20
Bibkey:
Cite (ACL):
Lasitha Uyangodage, Tharindu Ranasinghe, and Hansi Hettiarachchi. 2021. Transformers to Fight the COVID-19 Infodemic. In Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 130–135, Online. Association for Computational Linguistics.
Cite (Informal):
Transformers to Fight the COVID-19 Infodemic (Uyangodage et al., NLP4IF 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.nlp4if-1.20.pdf
Code
 tharindudr/infominer