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A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms

Fake news trends have overgrown in our societies over the years through social media platforms. The goal of spreading fake news can easily mislead and manipulate the public’s opinion. Many previous researchers have proposed this domain using classification algorithms or deep learning techniques. How...

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Main Authors: Yu, Chiung Chang, A Hamid, Isredza Rahmi, Abdullah, Zubaile, Kipli, Kuryati, Amnur, Hidra
Format: Article
Language:English
Published: Joiv 2024
Subjects:
QA Mathematics
Online Access:http://eprints.uthm.edu.my/12451/1/J17940_0625ba73b43987b4518cbcd1a9002c90.pdf
http://eprints.uthm.edu.my/12451/
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spelling my.uthm.eprints.124512025-01-27T02:48:25Z http://eprints.uthm.edu.my/12451/ A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms Yu, Chiung Chang A Hamid, Isredza Rahmi Abdullah, Zubaile Kipli, Kuryati Amnur, Hidra QA Mathematics Fake news trends have overgrown in our societies over the years through social media platforms. The goal of spreading fake news can easily mislead and manipulate the public’s opinion. Many previous researchers have proposed this domain using classification algorithms or deep learning techniques. However, machine learning algorithms still suffer from high margin error, which makes them unreliable as every algorithm uses a different way of prediction. Deep learning requires high computation power and a large dataset to operate the classification model. A filtering model with a consensus layer in a multi-tier model is introduced in this research paper. The multi-tier model filters the news label correctly predicted by the first two-tier layer. The consensus layer acts as the final decision when collision results occur in the first two-tier layer. The proposed model is applied to the WEKA software tool to test and evaluate the model from both datasets. Two sequences of classification models are used in this research paper: LR_DT_RF and LR_NB_AdaBoost. The best performance of sequence for both datasets is LR_DT_RF which yields 0.9892 F1-Score, 0.9895 Accuracy, and 0.9790 Matthews Correlation Coefficient (MCC) for ISOT Fake News Dataset, and 0.9913 F1-Score, 0.9853 Accuracy, and 0.9455 MCC for CHECKED Dataset. This research could give researchers an approach for fake news detection on different social platforms and feature-based. Joiv 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/12451/1/J17940_0625ba73b43987b4518cbcd1a9002c90.pdf Yu, Chiung Chang and A Hamid, Isredza Rahmi and Abdullah, Zubaile and Kipli, Kuryati and Amnur, Hidra (2024) A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms. International Journal On Informatics Visualization, 8 (2). pp. 643-651.
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Yu, Chiung Chang
A Hamid, Isredza Rahmi
Abdullah, Zubaile
Kipli, Kuryati
Amnur, Hidra
A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms
description Fake news trends have overgrown in our societies over the years through social media platforms. The goal of spreading fake news can easily mislead and manipulate the public’s opinion. Many previous researchers have proposed this domain using classification algorithms or deep learning techniques. However, machine learning algorithms still suffer from high margin error, which makes them unreliable as every algorithm uses a different way of prediction. Deep learning requires high computation power and a large dataset to operate the classification model. A filtering model with a consensus layer in a multi-tier model is introduced in this research paper. The multi-tier model filters the news label correctly predicted by the first two-tier layer. The consensus layer acts as the final decision when collision results occur in the first two-tier layer. The proposed model is applied to the WEKA software tool to test and evaluate the model from both datasets. Two sequences of classification models are used in this research paper: LR_DT_RF and LR_NB_AdaBoost. The best performance of sequence for both datasets is LR_DT_RF which yields 0.9892 F1-Score, 0.9895 Accuracy, and 0.9790 Matthews Correlation Coefficient (MCC) for ISOT Fake News Dataset, and 0.9913 F1-Score, 0.9853 Accuracy, and 0.9455 MCC for CHECKED Dataset. This research could give researchers an approach for fake news detection on different social platforms and feature-based.
format Article
author Yu, Chiung Chang
A Hamid, Isredza Rahmi
Abdullah, Zubaile
Kipli, Kuryati
Amnur, Hidra
author_facet Yu, Chiung Chang
A Hamid, Isredza Rahmi
Abdullah, Zubaile
Kipli, Kuryati
Amnur, Hidra
author_sort Yu, Chiung Chang
title A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms
title_short A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms
title_full A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms
title_fullStr A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms
title_full_unstemmed A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms
title_sort multi-tier model and filtering approach to detect fake news using machine learning algorithms
publisher Joiv
publishDate 2024
url http://eprints.uthm.edu.my/12451/1/J17940_0625ba73b43987b4518cbcd1a9002c90.pdf
http://eprints.uthm.edu.my/12451/
_version_ 1823094309437571072
score 13.23648

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