Transformers to Fight the COVID-19 Infodemic

Uyangodage, Lasitha and Ranasinghe, Tharindu and Hettiarachchi, Hansi (2021) Transformers to Fight the COVID-19 Infodemic. In: 4th Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, 1st June 2021, Online.

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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.

Item Type:	Conference or Workshop Item (Paper)
Identification Number:	10.18653/v1/2021.nlp4if-1.20
Dates:	Date Event 15 April 2021 Accepted 6 June 2021 Published
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Hansi Hettiarachchi
Date Deposited:	21 Dec 2021 15:43
Last Modified:	21 Dec 2021 15:43
URI:	https://www.open-access.bcu.ac.uk/id/eprint/12553

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