Transformers to Fight the COVID-19 Infodemic

Lasitha Uyangodage, Tharindu Ranasinghe, Hansi Hettiarachchi

    Research output: Chapter in Book/Published conference outputConference publication


    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.
    Original languageEnglish
    Title of host publicationProceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)
    PublisherAssociation for Computational Linguistics (ACL)
    Number of pages6
    Publication statusPublished - Jun 2021

    Bibliographical note

    Copyright © 2021 Association for Computational Linguistics.licensed on a Creative Commons Attribution 4.0 International License.


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