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Sentiment Analysis for Fake News Detection by Means of Neural Networks
The problem of fake news has become one of the most challenging issues having an impact on societies. Nowadays, false information may spread quickly through social media. In that regard, fake news needs to be detected as fast as possible to avoid negative influence on people who may rely on such inf...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303704/ http://dx.doi.org/10.1007/978-3-030-50423-6_49 |
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author | Kula, Sebastian Choraś, Michał Kozik, Rafał Ksieniewicz, Paweł Woźniak, Michał |
author_facet | Kula, Sebastian Choraś, Michał Kozik, Rafał Ksieniewicz, Paweł Woźniak, Michał |
author_sort | Kula, Sebastian |
collection | PubMed |
description | The problem of fake news has become one of the most challenging issues having an impact on societies. Nowadays, false information may spread quickly through social media. In that regard, fake news needs to be detected as fast as possible to avoid negative influence on people who may rely on such information while making important decisions (e.g., presidential elections). In this paper, we present an innovative solution for fake news detection that utilizes deep learning methods. Our experiments prove that the proposed approach allows us to achieve promising results. |
format | Online Article Text |
id | pubmed-7303704 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73037042020-06-19 Sentiment Analysis for Fake News Detection by Means of Neural Networks Kula, Sebastian Choraś, Michał Kozik, Rafał Ksieniewicz, Paweł Woźniak, Michał Computational Science – ICCS 2020 Article The problem of fake news has become one of the most challenging issues having an impact on societies. Nowadays, false information may spread quickly through social media. In that regard, fake news needs to be detected as fast as possible to avoid negative influence on people who may rely on such information while making important decisions (e.g., presidential elections). In this paper, we present an innovative solution for fake news detection that utilizes deep learning methods. Our experiments prove that the proposed approach allows us to achieve promising results. 2020-05-23 /pmc/articles/PMC7303704/ http://dx.doi.org/10.1007/978-3-030-50423-6_49 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Kula, Sebastian Choraś, Michał Kozik, Rafał Ksieniewicz, Paweł Woźniak, Michał Sentiment Analysis for Fake News Detection by Means of Neural Networks |
title | Sentiment Analysis for Fake News Detection by Means of Neural Networks |
title_full | Sentiment Analysis for Fake News Detection by Means of Neural Networks |
title_fullStr | Sentiment Analysis for Fake News Detection by Means of Neural Networks |
title_full_unstemmed | Sentiment Analysis for Fake News Detection by Means of Neural Networks |
title_short | Sentiment Analysis for Fake News Detection by Means of Neural Networks |
title_sort | sentiment analysis for fake news detection by means of neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303704/ http://dx.doi.org/10.1007/978-3-030-50423-6_49 |
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