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        <title>Automated Evidence Collection for Fake News Detection</title>
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            <namePart type="given">Sivaji</namePart>
            <namePart type="family">Bandyopadhyay</namePart>
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            <namePart type="given">Pushpak</namePart>
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    <abstract>Fake news, misinformation, and unverifiable facts on social media platforms propagate disharmony and affect society, especially when dealing with an epidemic like COVID-19. The task of Fake News Detection aims to tackle the effects of such misinformation by classifying news items as fake or real. In this paper, we propose a novel approach that improves over the current automatic fake news detection approaches by automatically gathering evidence for each claim. Our approach extracts supporting evidence from the web articles and then selects appropriate text to be treated as evidence sets. We use a pre-trained summarizer on these evidence sets and then use the extracted summary as supporting evidence to aid the classification task. Our experiments, using both machine learning and deep learning-based methods, help perform an extensive evaluation of our approach. The results show that our approach outperforms the state-of-the-art methods in fake news detection to achieve an F1-score of 99.25 over the dataset provided for the CONSTRAINT-2021 Shared Task. We also release the augmented dataset, our code and models for any further research.</abstract>
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%0 Conference Proceedings
%T Automated Evidence Collection for Fake News Detection
%A Rawat, Mrinal
%A Kanojia, Diptesh
%Y Bandyopadhyay, Sivaji
%Y Devi, Sobha Lalitha
%Y Bhattacharyya, Pushpak
%S Proceedings of the 18th International Conference on Natural Language Processing (ICON)
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C National Institute of Technology Silchar, Silchar, India
%F rawat-kanojia-2021-automated
%X Fake news, misinformation, and unverifiable facts on social media platforms propagate disharmony and affect society, especially when dealing with an epidemic like COVID-19. The task of Fake News Detection aims to tackle the effects of such misinformation by classifying news items as fake or real. In this paper, we propose a novel approach that improves over the current automatic fake news detection approaches by automatically gathering evidence for each claim. Our approach extracts supporting evidence from the web articles and then selects appropriate text to be treated as evidence sets. We use a pre-trained summarizer on these evidence sets and then use the extracted summary as supporting evidence to aid the classification task. Our experiments, using both machine learning and deep learning-based methods, help perform an extensive evaluation of our approach. The results show that our approach outperforms the state-of-the-art methods in fake news detection to achieve an F1-score of 99.25 over the dataset provided for the CONSTRAINT-2021 Shared Task. We also release the augmented dataset, our code and models for any further research.
%U https://aclanthology.org/2021.icon-main.55
%P 456-464

Markdown (Informal)

[Automated Evidence Collection for Fake News Detection](https://aclanthology.org/2021.icon-main.55) (Rawat & Kanojia, ICON 2021)

• Automated Evidence Collection for Fake News Detection (Rawat & Kanojia, ICON 2021)

ACL

• Mrinal Rawat and Diptesh Kanojia. 2021. Automated Evidence Collection for Fake News Detection. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 456–464, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).