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
- Venue:
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.nlp4if-1.20.pdf
- Code
- tharindudr/infominer