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Bio-Inspired Artificial Intelligence with Natural Language Processing Based on Deceptive Content Detection in Social Networking

In recent research, fake news detection in social networking using Machine Learning (ML) and Deep Learning (DL) models has gained immense attention. The current research article presents the Bio-inspired Artificial Intelligence with Natural Language Processing Deceptive Content Detection (BAINLP-DCD...

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Detalles Bibliográficos
Autores principales: Albraikan, Amani Abdulrahman, Maray, Mohammed, Alotaibi, Faiz Abdullah, Alnfiai, Mrim M., Kumar, Arun, Sayed, Ahmed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10604026/
https://www.ncbi.nlm.nih.gov/pubmed/37887580
http://dx.doi.org/10.3390/biomimetics8060449
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author Albraikan, Amani Abdulrahman
Maray, Mohammed
Alotaibi, Faiz Abdullah
Alnfiai, Mrim M.
Kumar, Arun
Sayed, Ahmed
author_facet Albraikan, Amani Abdulrahman
Maray, Mohammed
Alotaibi, Faiz Abdullah
Alnfiai, Mrim M.
Kumar, Arun
Sayed, Ahmed
author_sort Albraikan, Amani Abdulrahman
collection PubMed
description In recent research, fake news detection in social networking using Machine Learning (ML) and Deep Learning (DL) models has gained immense attention. The current research article presents the Bio-inspired Artificial Intelligence with Natural Language Processing Deceptive Content Detection (BAINLP-DCD) technique for social networking. The goal of the proposed BAINLP-DCD technique is to detect the presence of deceptive or fake content on social media. In order to accomplish this, the BAINLP-DCD algorithm applies data preprocessing to transform the input dataset into a meaningful format. For deceptive content detection, the BAINLP-DCD technique uses a Multi-Head Self-attention Bi-directional Long Short-Term Memory (MHS-BiLSTM) model. Finally, the African Vulture Optimization Algorithm (AVOA) is applied for the selection of optimum hyperparameters of the MHS-BiLSTM model. The proposed BAINLP-DCD algorithm was validated through simulation using two benchmark fake news datasets. The experimental outcomes portrayed the enhanced performance of the BAINLP-DCD technique, with maximum accuracy values of 92.19% and 92.56% on the BuzzFeed and PolitiFact datasets, respectively.
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spelling pubmed-106040262023-10-28 Bio-Inspired Artificial Intelligence with Natural Language Processing Based on Deceptive Content Detection in Social Networking Albraikan, Amani Abdulrahman Maray, Mohammed Alotaibi, Faiz Abdullah Alnfiai, Mrim M. Kumar, Arun Sayed, Ahmed Biomimetics (Basel) Article In recent research, fake news detection in social networking using Machine Learning (ML) and Deep Learning (DL) models has gained immense attention. The current research article presents the Bio-inspired Artificial Intelligence with Natural Language Processing Deceptive Content Detection (BAINLP-DCD) technique for social networking. The goal of the proposed BAINLP-DCD technique is to detect the presence of deceptive or fake content on social media. In order to accomplish this, the BAINLP-DCD algorithm applies data preprocessing to transform the input dataset into a meaningful format. For deceptive content detection, the BAINLP-DCD technique uses a Multi-Head Self-attention Bi-directional Long Short-Term Memory (MHS-BiLSTM) model. Finally, the African Vulture Optimization Algorithm (AVOA) is applied for the selection of optimum hyperparameters of the MHS-BiLSTM model. The proposed BAINLP-DCD algorithm was validated through simulation using two benchmark fake news datasets. The experimental outcomes portrayed the enhanced performance of the BAINLP-DCD technique, with maximum accuracy values of 92.19% and 92.56% on the BuzzFeed and PolitiFact datasets, respectively. MDPI 2023-09-23 /pmc/articles/PMC10604026/ /pubmed/37887580 http://dx.doi.org/10.3390/biomimetics8060449 Text en © 2023 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
Albraikan, Amani Abdulrahman
Maray, Mohammed
Alotaibi, Faiz Abdullah
Alnfiai, Mrim M.
Kumar, Arun
Sayed, Ahmed
Bio-Inspired Artificial Intelligence with Natural Language Processing Based on Deceptive Content Detection in Social Networking
title Bio-Inspired Artificial Intelligence with Natural Language Processing Based on Deceptive Content Detection in Social Networking
title_full Bio-Inspired Artificial Intelligence with Natural Language Processing Based on Deceptive Content Detection in Social Networking
title_fullStr Bio-Inspired Artificial Intelligence with Natural Language Processing Based on Deceptive Content Detection in Social Networking
title_full_unstemmed Bio-Inspired Artificial Intelligence with Natural Language Processing Based on Deceptive Content Detection in Social Networking
title_short Bio-Inspired Artificial Intelligence with Natural Language Processing Based on Deceptive Content Detection in Social Networking
title_sort bio-inspired artificial intelligence with natural language processing based on deceptive content detection in social networking
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10604026/
https://www.ncbi.nlm.nih.gov/pubmed/37887580
http://dx.doi.org/10.3390/biomimetics8060449
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