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        <title>Graph-based Fake News Detection using a Summarization Technique</title>
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            <namePart type="given">Paola</namePart>
            <namePart type="family">Merlo</namePart>
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    <abstract>Nowadays, fake news is spreading in various ways, and this fake information is causing a lot of social damages. Thus the need to detect fake information is increasing to prevent the damages caused by fake news. In this paper, we propose a novel graph-based fake news detection method using a summarization technique that uses only the document internal information. Our proposed method represents the relationship between all sentences using a graph and the reflection rate of contextual information among sentences is computed by using an attention mechanism. In addition, we improve the performance of fake news detection by utilizing summary information as an important subject of the document. The experimental results demonstrate that our method achieves high accuracy, 91.04%, that is 8.85%p better than the previous method.</abstract>
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    <identifier type="doi">10.18653/v1/2021.eacl-main.287</identifier>
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        <url>https://aclanthology.org/2021.eacl-main.287</url>
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        <date>2021-04</date>
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%0 Conference Proceedings
%T Graph-based Fake News Detection using a Summarization Technique
%A Kim, Gihwan
%A Ko, Youngjoong
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F kim-ko-2021-graph
%X Nowadays, fake news is spreading in various ways, and this fake information is causing a lot of social damages. Thus the need to detect fake information is increasing to prevent the damages caused by fake news. In this paper, we propose a novel graph-based fake news detection method using a summarization technique that uses only the document internal information. Our proposed method represents the relationship between all sentences using a graph and the reflection rate of contextual information among sentences is computed by using an attention mechanism. In addition, we improve the performance of fake news detection by utilizing summary information as an important subject of the document. The experimental results demonstrate that our method achieves high accuracy, 91.04%, that is 8.85%p better than the previous method.
%R 10.18653/v1/2021.eacl-main.287
%U https://aclanthology.org/2021.eacl-main.287
%U https://doi.org/10.18653/v1/2021.eacl-main.287
%P 3276-3280

Markdown (Informal)

[Graph-based Fake News Detection using a Summarization Technique](https://aclanthology.org/2021.eacl-main.287) (Kim & Ko, EACL 2021)

• Graph-based Fake News Detection using a Summarization Technique (Kim & Ko, EACL 2021)

ACL

• Gihwan Kim and Youngjoong Ko. 2021. Graph-based Fake News Detection using a Summarization Technique. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3276–3280, Online. Association for Computational Linguistics.