Transformers to Fight the COVID-19 Infodemic


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.

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Additional Information: Copyright © 2021 Association for Computational Linguistics.licensed on a Creative Commons Attribution 4.0 International License.
Last Modified: 27 Jun 2024 12:36
Date Deposited: 15 May 2023 10:59
Full Text Link: https://www.len ... 428-912-739-287
https://arxiv.o ... /abs/2104.12201
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PURE Output Type: Conference contribution
Published Date: 2021-06
Authors: Uyangodage, Lasitha
Ranasinghe, Tharindu (ORCID Profile 0000-0003-3207-3821)
Hettiarachchi, Hansi



Version: Published Version

License: Creative Commons Attribution

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