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MCred: multi-modal message credibility for fake news detection using BERT and CNN
Online social media enables low cost, easy access, rapid propagation, and easy communication of information, including spreading low-quality fake news. Fake news has become a huge threat to every sector in society, and resulting in decrements in the trust quotient for media and leading the audience...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9325949/ https://www.ncbi.nlm.nih.gov/pubmed/35910294 http://dx.doi.org/10.1007/s12652-022-04338-2 |
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author | Verma, Pawan Kumar Agrawal, Prateek Madaan, Vishu Prodan, Radu |
author_facet | Verma, Pawan Kumar Agrawal, Prateek Madaan, Vishu Prodan, Radu |
author_sort | Verma, Pawan Kumar |
collection | PubMed |
description | Online social media enables low cost, easy access, rapid propagation, and easy communication of information, including spreading low-quality fake news. Fake news has become a huge threat to every sector in society, and resulting in decrements in the trust quotient for media and leading the audience into bewilderment. In this paper, we proposed a new framework called Message Credibility (MCred) for fake news detection that utilizes the benefits of local and global text semantics. This framework is the fusion of Bidirectional Encoder Representations from Transformers (BERT) using the relationship between words in sentences for global text semantics, and Convolutional Neural Networks (CNN) using N-gram features for local text semantics. We demonstrate through experimental results a popular Kaggle dataset that MCred improves the accuracy over a state-of-the-art model by 1.10% thanks to its combination of local and global text semantics. |
format | Online Article Text |
id | pubmed-9325949 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-93259492022-07-27 MCred: multi-modal message credibility for fake news detection using BERT and CNN Verma, Pawan Kumar Agrawal, Prateek Madaan, Vishu Prodan, Radu J Ambient Intell Humaniz Comput Original Research Online social media enables low cost, easy access, rapid propagation, and easy communication of information, including spreading low-quality fake news. Fake news has become a huge threat to every sector in society, and resulting in decrements in the trust quotient for media and leading the audience into bewilderment. In this paper, we proposed a new framework called Message Credibility (MCred) for fake news detection that utilizes the benefits of local and global text semantics. This framework is the fusion of Bidirectional Encoder Representations from Transformers (BERT) using the relationship between words in sentences for global text semantics, and Convolutional Neural Networks (CNN) using N-gram features for local text semantics. We demonstrate through experimental results a popular Kaggle dataset that MCred improves the accuracy over a state-of-the-art model by 1.10% thanks to its combination of local and global text semantics. Springer Berlin Heidelberg 2022-07-27 /pmc/articles/PMC9325949/ /pubmed/35910294 http://dx.doi.org/10.1007/s12652-022-04338-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Research Verma, Pawan Kumar Agrawal, Prateek Madaan, Vishu Prodan, Radu MCred: multi-modal message credibility for fake news detection using BERT and CNN |
title | MCred: multi-modal message credibility for fake news detection using BERT and CNN |
title_full | MCred: multi-modal message credibility for fake news detection using BERT and CNN |
title_fullStr | MCred: multi-modal message credibility for fake news detection using BERT and CNN |
title_full_unstemmed | MCred: multi-modal message credibility for fake news detection using BERT and CNN |
title_short | MCred: multi-modal message credibility for fake news detection using BERT and CNN |
title_sort | mcred: multi-modal message credibility for fake news detection using bert and cnn |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9325949/ https://www.ncbi.nlm.nih.gov/pubmed/35910294 http://dx.doi.org/10.1007/s12652-022-04338-2 |
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