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    <titleInfo>
        <title>Detecting Multilingual COVID-19 Misinformation on Social Media via Contextualized Embeddings</title>
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    <name type="personal">
        <namePart type="given">Subhadarshi</namePart>
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        <namePart type="family">Levitan</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>We present machine learning classifiers to automatically identify COVID-19 misinformation on social media in three languages: English, Bulgarian, and Arabic. We compared 4 multitask learning models for this task and found that a model trained with English BERT achieves the best results for English, and multilingual BERT achieves the best results for Bulgarian and Arabic. We experimented with zero shot, few shot, and target-only conditions to evaluate the impact of target-language training data on classifier performance, and to understand the capabilities of different models to generalize across languages in detecting misinformation online. This work was performed as a submission to the shared task, NLP4IF 2021: Fighting the COVID-19 Infodemic. Our best models achieved the second best evaluation test results for Bulgarian and Arabic among all the participating teams and obtained competitive scores for English.</abstract>
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    <identifier type="doi">10.18653/v1/2021.nlp4if-1.19</identifier>
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%0 Conference Proceedings
%T Detecting Multilingual COVID-19 Misinformation on Social Media via Contextualized Embeddings
%A Panda, Subhadarshi
%A Levitan, Sarah Ita
%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 panda-levitan-2021-detecting
%X We present machine learning classifiers to automatically identify COVID-19 misinformation on social media in three languages: English, Bulgarian, and Arabic. We compared 4 multitask learning models for this task and found that a model trained with English BERT achieves the best results for English, and multilingual BERT achieves the best results for Bulgarian and Arabic. We experimented with zero shot, few shot, and target-only conditions to evaluate the impact of target-language training data on classifier performance, and to understand the capabilities of different models to generalize across languages in detecting misinformation online. This work was performed as a submission to the shared task, NLP4IF 2021: Fighting the COVID-19 Infodemic. Our best models achieved the second best evaluation test results for Bulgarian and Arabic among all the participating teams and obtained competitive scores for English.
%R 10.18653/v1/2021.nlp4if-1.19
%U https://aclanthology.org/2021.nlp4if-1.19/
%U https://doi.org/10.18653/v1/2021.nlp4if-1.19
%P 125-129

Markdown (Informal)

[Detecting Multilingual COVID-19 Misinformation on Social Media via Contextualized Embeddings](https://aclanthology.org/2021.nlp4if-1.19/) (Panda & Levitan, NLP4IF 2021)

• Detecting Multilingual COVID-19 Misinformation on Social Media via Contextualized Embeddings (Panda & Levitan, NLP4IF 2021)

ACL

• Subhadarshi Panda and Sarah Ita Levitan. 2021. Detecting Multilingual COVID-19 Misinformation on Social Media via Contextualized Embeddings. In Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 125–129, Online. Association for Computational Linguistics.