Can Multilingual Transformers Fight the COVID-19 Infodemic?

Uyangodage, Lasitha and Ranasinghe, Tharindu and Hettiarachchi, Hansi (2021) Can Multilingual Transformers Fight the COVID-19 Infodemic? In: International Conference on Recent Advances in Natural Language Processing (RANLP 2021), 1st September 2021, Online.

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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. In recent years, supervised machine learning models have been used to automatically identify false information in social media. However, most of these machine learning models focus only on the language they were trained on. Given the fact that social media platforms are being used in different languages, managing machine learning models for each and every language separately would be chaotic. In this research, we experiment with multilingual models to identify false information in social media by using two recently released multilingual false information detection datasets. We show that multilingual models perform on par with the monolingual models and sometimes even better than the monolingual models to detect false information in social media making them more useful in real-world scenarios.

Item Type: Conference or Workshop Item (Paper)
26 July 2021Accepted
3 September 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 15:53
Last Modified: 21 Dec 2021 15:53

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