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: https://doi.org/10.18653/v1/2021.nlp4if-1.20
Dates:
DateEvent
15 April 2021Accepted
6 June 2021Published
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Hansi Hettiarachchi
Date Deposited: 21 Dec 2021 15:43
Last Modified: 21 Dec 2021 15:43
URI: http://www.open-access.bcu.ac.uk/id/eprint/12553

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