COVID-19 Infodemic in Malaysia: Conceptualizing Fake News for Detection
There is an “Infodemic” of COVID-19 in which there are a lot of rumours and information disorders spreading rapidly, the purpose of the study is to build a predictive model for identifying whether the COVID-19 information in the Malay language in Malaysia is real or fake. Under the study of COVID-19...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2023/9629700 |
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| _version_ | 1832548021356724224 |
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| author | Chee Kuan Lim Zurinahni Zainol Bahiyah Omar Noor Farizah Ibrahim |
| author_facet | Chee Kuan Lim Zurinahni Zainol Bahiyah Omar Noor Farizah Ibrahim |
| author_sort | Chee Kuan Lim |
| collection | DOAJ |
| description | There is an “Infodemic” of COVID-19 in which there are a lot of rumours and information disorders spreading rapidly, the purpose of the study is to build a predictive model for identifying whether the COVID-19 information in the Malay language in Malaysia is real or fake. Under the study of COVID-19 fake news detection, the synthetic minority oversampling technique (SMOTE) is used to generate synthetic instances of real news in the training set after natural language processing (NLP) and before data modelling because the number of fake news is approximately three times greater than that of real news. Logistic regression, Naïve Bayes, decision trees, support vector machines, random forests, and gradient boosting are employed and compared to determine the most suitable predictive model. In short, the gradient-boosting classifier model has the highest value of accuracy and F1-score. |
| format | Article |
| id | doaj-art-e2ba033ac08e4aa0b5dab0b73dbcdb3c |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-e2ba033ac08e4aa0b5dab0b73dbcdb3c2025-02-03T06:42:44ZengWileyAdvances in Multimedia1687-56992023-01-01202310.1155/2023/9629700COVID-19 Infodemic in Malaysia: Conceptualizing Fake News for DetectionChee Kuan Lim0Zurinahni Zainol1Bahiyah Omar2Noor Farizah Ibrahim3School of Computer SciencesSchool of Computer SciencesSchool of CommunicationSchool of Computer SciencesThere is an “Infodemic” of COVID-19 in which there are a lot of rumours and information disorders spreading rapidly, the purpose of the study is to build a predictive model for identifying whether the COVID-19 information in the Malay language in Malaysia is real or fake. Under the study of COVID-19 fake news detection, the synthetic minority oversampling technique (SMOTE) is used to generate synthetic instances of real news in the training set after natural language processing (NLP) and before data modelling because the number of fake news is approximately three times greater than that of real news. Logistic regression, Naïve Bayes, decision trees, support vector machines, random forests, and gradient boosting are employed and compared to determine the most suitable predictive model. In short, the gradient-boosting classifier model has the highest value of accuracy and F1-score.http://dx.doi.org/10.1155/2023/9629700 |
| spellingShingle | Chee Kuan Lim Zurinahni Zainol Bahiyah Omar Noor Farizah Ibrahim COVID-19 Infodemic in Malaysia: Conceptualizing Fake News for Detection Advances in Multimedia |
| title | COVID-19 Infodemic in Malaysia: Conceptualizing Fake News for Detection |
| title_full | COVID-19 Infodemic in Malaysia: Conceptualizing Fake News for Detection |
| title_fullStr | COVID-19 Infodemic in Malaysia: Conceptualizing Fake News for Detection |
| title_full_unstemmed | COVID-19 Infodemic in Malaysia: Conceptualizing Fake News for Detection |
| title_short | COVID-19 Infodemic in Malaysia: Conceptualizing Fake News for Detection |
| title_sort | covid 19 infodemic in malaysia conceptualizing fake news for detection |
| url | http://dx.doi.org/10.1155/2023/9629700 |
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