Covid-19 Misinformation Classification On Twitter In Malaysia Using A Hybrid Adaptive Neuro-Fuzzy Inferences System (Anfis) And Deep Neural Network (Dnn)

The spread of Covid-19 misinformation on social media had significant real-world consequences, raising fears among internet users since the pandemic has begun. Worldwide, researchers have shown an interest in developing deception classification methods to reduce the issue. This study aims to create...

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Main Author:	Ravichandran, Bhavani Devi
Format:	Thesis
Language:	English
Published:	2023
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/60524/1/BALBER%20SINGH%20AL%20HADEP%20SINGH%20-%20TESIS%20cut.pdf
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id	my-usm-ep.60524
record_format	uketd_dc
spelling	my-usm-ep.605242024-05-03T07:17:21Z Covid-19 Misinformation Classification On Twitter In Malaysia Using A Hybrid Adaptive Neuro-Fuzzy Inferences System (Anfis) And Deep Neural Network (Dnn) 2023-01 Ravichandran, Bhavani Devi QA75.5-76.95 Electronic computers. Computer science The spread of Covid-19 misinformation on social media had significant real-world consequences, raising fears among internet users since the pandemic has begun. Worldwide, researchers have shown an interest in developing deception classification methods to reduce the issue. This study aims to create an accurate model for the classification of Covid-19 misinformation in social media. This research has also conducted a systematic literature review to identify the most efficient method for classification with 35 papers. According to existing studies, the most efficient method for classification with the highest accuracy is the ANFIS and the DNN models. Thus, it was identified that the hybrid model of ANFIS-DNN shows the highest accuracy results. Therefore, the main goal of this study is to classify Covid-19 misinformation using an optimised hybrid model of ANFIS-DNN on social media based on the level of risk. A total of 8,000 Malaysian-based Tweets were extracted from Twitter based on topics related to Covid-19. The dataset is explored, cleaned, pre-processed, and the tweets were grouped into BoW model. Then, the proposed ANFIS-DNN is used to run the pre-processed dataset and the accuracy performance result shows 99%. Evaluation performance indexes such as confusion matrix, and accuracy are implemented in this research. The proposed model is then compared with ANFIS, DNN, Logistic Regression, SVM, Random Forest, and XGBoost. Furthermore, the accuracy is compared with other related works. 2023-01 Thesis http://eprints.usm.my/60524/ http://eprints.usm.my/60524/1/BALBER%20SINGH%20AL%20HADEP%20SINGH%20-%20TESIS%20cut.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Ravichandran, Bhavani Devi Covid-19 Misinformation Classification On Twitter In Malaysia Using A Hybrid Adaptive Neuro-Fuzzy Inferences System (Anfis) And Deep Neural Network (Dnn)
description	The spread of Covid-19 misinformation on social media had significant real-world consequences, raising fears among internet users since the pandemic has begun. Worldwide, researchers have shown an interest in developing deception classification methods to reduce the issue. This study aims to create an accurate model for the classification of Covid-19 misinformation in social media. This research has also conducted a systematic literature review to identify the most efficient method for classification with 35 papers. According to existing studies, the most efficient method for classification with the highest accuracy is the ANFIS and the DNN models. Thus, it was identified that the hybrid model of ANFIS-DNN shows the highest accuracy results. Therefore, the main goal of this study is to classify Covid-19 misinformation using an optimised hybrid model of ANFIS-DNN on social media based on the level of risk. A total of 8,000 Malaysian-based Tweets were extracted from Twitter based on topics related to Covid-19. The dataset is explored, cleaned, pre-processed, and the tweets were grouped into BoW model. Then, the proposed ANFIS-DNN is used to run the pre-processed dataset and the accuracy performance result shows 99%. Evaluation performance indexes such as confusion matrix, and accuracy are implemented in this research. The proposed model is then compared with ANFIS, DNN, Logistic Regression, SVM, Random Forest, and XGBoost. Furthermore, the accuracy is compared with other related works.
format	Thesis
qualification_level	Master's degree
author	Ravichandran, Bhavani Devi
author_facet	Ravichandran, Bhavani Devi
author_sort	Ravichandran, Bhavani Devi
title	Covid-19 Misinformation Classification On Twitter In Malaysia Using A Hybrid Adaptive Neuro-Fuzzy Inferences System (Anfis) And Deep Neural Network (Dnn)
title_short	Covid-19 Misinformation Classification On Twitter In Malaysia Using A Hybrid Adaptive Neuro-Fuzzy Inferences System (Anfis) And Deep Neural Network (Dnn)
title_full	Covid-19 Misinformation Classification On Twitter In Malaysia Using A Hybrid Adaptive Neuro-Fuzzy Inferences System (Anfis) And Deep Neural Network (Dnn)
title_fullStr	Covid-19 Misinformation Classification On Twitter In Malaysia Using A Hybrid Adaptive Neuro-Fuzzy Inferences System (Anfis) And Deep Neural Network (Dnn)
title_full_unstemmed	Covid-19 Misinformation Classification On Twitter In Malaysia Using A Hybrid Adaptive Neuro-Fuzzy Inferences System (Anfis) And Deep Neural Network (Dnn)
title_sort	covid-19 misinformation classification on twitter in malaysia using a hybrid adaptive neuro-fuzzy inferences system (anfis) and deep neural network (dnn)
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer
publishDate	2023
url	http://eprints.usm.my/60524/1/BALBER%20SINGH%20AL%20HADEP%20SINGH%20-%20TESIS%20cut.pdf
_version_	1804888955846393856

Covid-19 Misinformation Classification On Twitter In Malaysia Using A Hybrid Adaptive Neuro-Fuzzy Inferences System (Anfis) And Deep Neural Network (Dnn)

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