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        <title>Automatic Fake News Detection in Political Platforms - A Transformer-based Approach</title>
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        <namePart type="given">Shaina</namePart>
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            <title>Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)</title>
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        <name type="personal">
            <namePart type="given">Ali</namePart>
            <namePart type="family">Hürriyetoğlu</namePart>
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    <abstract>The dynamics and influence of fake news on Twitter during the 2020 US presidential election remains to be clarified. Here, we use a dataset related to 2020 U.S Election that consists of news articles and tweets on those articles. Therefore, it is extremely important to stop the spread of fake news before it reaches a mass level, which is a big challenge. We propose a novel fake news detection framework that can address this challenge. Our proposed framework exploits the information from news articles and social contexts to detect fake news. The proposed model is based on a Transformer architecture, which can learn useful representations from fake news data and predicts the probability of a news as being fake or real. Experimental results on real-world data show that our model can detect fake news with higher accuracy and much earlier, compared to the baselines.</abstract>
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    <identifier type="doi">10.18653/v1/2021.case-1.10</identifier>
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        <date>2021-08</date>
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            <start>68</start>
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%0 Conference Proceedings
%T Automatic Fake News Detection in Political Platforms - A Transformer-based Approach
%A Raza, Shaina
%Y Hürriyetoğlu, Ali
%S Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F raza-2021-automatic
%X The dynamics and influence of fake news on Twitter during the 2020 US presidential election remains to be clarified. Here, we use a dataset related to 2020 U.S Election that consists of news articles and tweets on those articles. Therefore, it is extremely important to stop the spread of fake news before it reaches a mass level, which is a big challenge. We propose a novel fake news detection framework that can address this challenge. Our proposed framework exploits the information from news articles and social contexts to detect fake news. The proposed model is based on a Transformer architecture, which can learn useful representations from fake news data and predicts the probability of a news as being fake or real. Experimental results on real-world data show that our model can detect fake news with higher accuracy and much earlier, compared to the baselines.
%R 10.18653/v1/2021.case-1.10
%U https://aclanthology.org/2021.case-1.10
%U https://doi.org/10.18653/v1/2021.case-1.10
%P 68-78

Markdown (Informal)

[Automatic Fake News Detection in Political Platforms - A Transformer-based Approach](https://aclanthology.org/2021.case-1.10) (Raza, CASE 2021)

• Automatic Fake News Detection in Political Platforms - A Transformer-based Approach (Raza, CASE 2021)

ACL

• Shaina Raza. 2021. Automatic Fake News Detection in Political Platforms - A Transformer-based Approach. In Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021), pages 68–78, Online. Association for Computational Linguistics.