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        <title>Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking</title>
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        <namePart type="given">Sundeep</namePart>
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            <title>Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda</title>
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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>
            <namePart type="family">Brew</namePart>
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        <name type="personal">
            <namePart type="given">Giovanni</namePart>
            <namePart type="given">Luca</namePart>
            <namePart type="family">Ciampaglia</namePart>
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            <namePart type="given">Anna</namePart>
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    <abstract>The rapid advancement of technology in online communication via social media platforms has led to a prolific rise in the spread of misinformation and fake news. Fake news is especially rampant in the current COVID-19 pandemic, leading to people believing in false and potentially harmful claims and stories. Detecting fake news quickly can alleviate the spread of panic, chaos and potential health hazards. We developed a two stage automated pipeline for COVID-19 fake news detection using state of the art machine learning models for natural language processing. The first model leverages a novel fact checking algorithm that retrieves the most relevant facts concerning user queries about particular COVID-19 claims. The second model verifies the level of “truth” in the queried claim by computing the textual entailment between the claim and the true facts retrieved from a manually curated COVID-19 dataset. The dataset is based on a publicly available knowledge source consisting of more than 5000 COVID-19 false claims and verified explanations, a subset of which was internally annotated and cross-validated to train and evaluate our models. We evaluate a series of models based on classical text-based features to more contextual Transformer based models and observe that a model pipeline based on BERT and ALBERT for the two stages respectively yields the best results.</abstract>
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%0 Conference Proceedings
%T Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking
%A Vijjali, Rutvik
%A Potluri, Prathyush
%A Kumar, Siddharth
%A Teki, Sundeep
%Y Da San Martino, Giovanni
%Y Brew, Chris
%Y Ciampaglia, Giovanni Luca
%Y Feldman, Anna
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2020
%8 December
%I International Committee on Computational Linguistics (ICCL)
%C Barcelona, Spain (Online)
%F vijjali-etal-2020-two
%X The rapid advancement of technology in online communication via social media platforms has led to a prolific rise in the spread of misinformation and fake news. Fake news is especially rampant in the current COVID-19 pandemic, leading to people believing in false and potentially harmful claims and stories. Detecting fake news quickly can alleviate the spread of panic, chaos and potential health hazards. We developed a two stage automated pipeline for COVID-19 fake news detection using state of the art machine learning models for natural language processing. The first model leverages a novel fact checking algorithm that retrieves the most relevant facts concerning user queries about particular COVID-19 claims. The second model verifies the level of “truth” in the queried claim by computing the textual entailment between the claim and the true facts retrieved from a manually curated COVID-19 dataset. The dataset is based on a publicly available knowledge source consisting of more than 5000 COVID-19 false claims and verified explanations, a subset of which was internally annotated and cross-validated to train and evaluate our models. We evaluate a series of models based on classical text-based features to more contextual Transformer based models and observe that a model pipeline based on BERT and ALBERT for the two stages respectively yields the best results.
%U https://aclanthology.org/2020.nlp4if-1.1
%P 1-10

Markdown (Informal)

[Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking](https://aclanthology.org/2020.nlp4if-1.1) (Vijjali et al., NLP4IF 2020)

• Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking (Vijjali et al., NLP4IF 2020)

ACL

• Rutvik Vijjali, Prathyush Potluri, Siddharth Kumar, and Sundeep Teki. 2020. Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking. In Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 1–10, Barcelona, Spain (Online). International Committee on Computational Linguistics (ICCL).