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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| Format: | Thesis |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://eprints.usm.my/60524/1/BALBER%20SINGH%20AL%20HADEP%20SINGH%20-%20TESIS%20cut.pdf http://eprints.usm.my/60524/ |
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| Summary: | 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. |
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