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Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique
Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community’s behavior. Researchers and social media service providers have c...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9504299/ https://www.ncbi.nlm.nih.gov/pubmed/36146319 http://dx.doi.org/10.3390/s22186970 |
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author | Ali, Abdullah Marish Ghaleb, Fuad A. Al-Rimy, Bander Ali Saleh Alsolami, Fawaz Jaber Khan, Asif Irshad |
author_facet | Ali, Abdullah Marish Ghaleb, Fuad A. Al-Rimy, Bander Ali Saleh Alsolami, Fawaz Jaber Khan, Asif Irshad |
author_sort | Ali, Abdullah Marish |
collection | PubMed |
description | Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community’s behavior. Researchers and social media service providers have commonly utilized artificial intelligence techniques in the recent few years to rein in fake news propagation. However, fake news detection is challenging due to the use of political language and the high linguistic similarities between real and fake news. In addition, most news sentences are short, therefore finding valuable representative features that machine learning classifiers can use to distinguish between fake and authentic news is difficult because both false and legitimate news have comparable language traits. Existing fake news solutions suffer from low detection performance due to improper representation and model design. This study aims at improving the detection accuracy by proposing a deep ensemble fake news detection model using the sequential deep learning technique. The proposed model was constructed in three phases. In the first phase, features were extracted from news contents, preprocessed using natural language processing techniques, enriched using n-gram, and represented using the term frequency–inverse term frequency technique. In the second phase, an ensemble model based on deep learning was constructed as follows. Multiple binary classifiers were trained using sequential deep learning networks to extract the representative hidden features that could accurately classify news types. In the third phase, a multi-class classifier was constructed based on multilayer perceptron (MLP) and trained using the features extracted from the aggregated outputs of the deep learning-based binary classifiers for final classification. The two popular and well-known datasets (LIAR and ISOT) were used with different classifiers to benchmark the proposed model. Compared with the state-of-the-art models, which use deep contextualized representation with convolutional neural network (CNN), the proposed model shows significant improvements (2.41%) in the overall performance in terms of the F1score for the LIAR dataset, which is more challenging than other datasets. Meanwhile, the proposed model achieves 100% accuracy with ISOT. The study demonstrates that traditional features extracted from news content with proper model design outperform the existing models that were constructed based on text embedding techniques. |
format | Online Article Text |
id | pubmed-9504299 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95042992022-09-24 Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique Ali, Abdullah Marish Ghaleb, Fuad A. Al-Rimy, Bander Ali Saleh Alsolami, Fawaz Jaber Khan, Asif Irshad Sensors (Basel) Article Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community’s behavior. Researchers and social media service providers have commonly utilized artificial intelligence techniques in the recent few years to rein in fake news propagation. However, fake news detection is challenging due to the use of political language and the high linguistic similarities between real and fake news. In addition, most news sentences are short, therefore finding valuable representative features that machine learning classifiers can use to distinguish between fake and authentic news is difficult because both false and legitimate news have comparable language traits. Existing fake news solutions suffer from low detection performance due to improper representation and model design. This study aims at improving the detection accuracy by proposing a deep ensemble fake news detection model using the sequential deep learning technique. The proposed model was constructed in three phases. In the first phase, features were extracted from news contents, preprocessed using natural language processing techniques, enriched using n-gram, and represented using the term frequency–inverse term frequency technique. In the second phase, an ensemble model based on deep learning was constructed as follows. Multiple binary classifiers were trained using sequential deep learning networks to extract the representative hidden features that could accurately classify news types. In the third phase, a multi-class classifier was constructed based on multilayer perceptron (MLP) and trained using the features extracted from the aggregated outputs of the deep learning-based binary classifiers for final classification. The two popular and well-known datasets (LIAR and ISOT) were used with different classifiers to benchmark the proposed model. Compared with the state-of-the-art models, which use deep contextualized representation with convolutional neural network (CNN), the proposed model shows significant improvements (2.41%) in the overall performance in terms of the F1score for the LIAR dataset, which is more challenging than other datasets. Meanwhile, the proposed model achieves 100% accuracy with ISOT. The study demonstrates that traditional features extracted from news content with proper model design outperform the existing models that were constructed based on text embedding techniques. MDPI 2022-09-15 /pmc/articles/PMC9504299/ /pubmed/36146319 http://dx.doi.org/10.3390/s22186970 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ali, Abdullah Marish Ghaleb, Fuad A. Al-Rimy, Bander Ali Saleh Alsolami, Fawaz Jaber Khan, Asif Irshad Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique |
title | Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique |
title_full | Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique |
title_fullStr | Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique |
title_full_unstemmed | Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique |
title_short | Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique |
title_sort | deep ensemble fake news detection model using sequential deep learning technique |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9504299/ https://www.ncbi.nlm.nih.gov/pubmed/36146319 http://dx.doi.org/10.3390/s22186970 |
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