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    <titleInfo>
        <title>Transformers to Fight the COVID-19 Infodemic</title>
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    <name type="personal">
        <namePart type="given">Lasitha</namePart>
        <namePart type="family">Uyangodage</namePart>
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    <name type="personal">
        <namePart type="given">Tharindu</namePart>
        <namePart type="family">Ranasinghe</namePart>
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    <name type="personal">
        <namePart type="given">Hansi</namePart>
        <namePart type="family">Hettiarachchi</namePart>
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            <title>Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda</title>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Feldman</namePart>
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        <name type="personal">
            <namePart type="given">Giovanni</namePart>
            <namePart type="family">Da San Martino</namePart>
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            <namePart type="given">Chris</namePart>
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            <namePart type="given">Preslav</namePart>
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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 4^th place in all the languages.</abstract>
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    <identifier type="doi">10.18653/v1/2021.nlp4if-1.20</identifier>
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        <date>2021-06</date>
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            <start>130</start>
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%0 Conference Proceedings
%T Transformers to Fight the COVID-19 Infodemic
%A Uyangodage, Lasitha
%A Ranasinghe, Tharindu
%A Hettiarachchi, Hansi
%Y Feldman, Anna
%Y Da San Martino, Giovanni
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F uyangodage-etal-2021-transformers
%X 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 4^th place in all the languages.
%R 10.18653/v1/2021.nlp4if-1.20
%U https://aclanthology.org/2021.nlp4if-1.20
%U https://doi.org/10.18653/v1/2021.nlp4if-1.20
%P 130-135

Markdown (Informal)

[Transformers to Fight the COVID-19 Infodemic](https://aclanthology.org/2021.nlp4if-1.20) (Uyangodage et al., NLP4IF 2021)

• Transformers to Fight the COVID-19 Infodemic (Uyangodage et al., NLP4IF 2021)

ACL

• Lasitha Uyangodage, Tharindu Ranasinghe, and Hansi Hettiarachchi. 2021. Transformers to Fight the COVID-19 Infodemic. In Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 130–135, Online. Association for Computational Linguistics.