Fakebuster: fake news detection system using logistic regression technique in machine learning
The uncontrollable spread of fake news through the net is irresistible in this globalization era. Fake news dissemination cannot be tolerated as the bad impacts of it to the society is really worrying. Furthermore, this will lead to more significant problems and potential threat such as confusion, m...
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| Format: | Article |
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Blue Eyes Intelligence Engineering & Sciences Publication
2019
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| _version_ | 1796980686615216128 |
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| author | Mokhtar, Muhammad Syahmi Jusoh, Yusmadi Yah Admodisastro, Novia Che Pa, Noraini Amruddin, Amru Yusrin |
| author_facet | Mokhtar, Muhammad Syahmi Jusoh, Yusmadi Yah Admodisastro, Novia Che Pa, Noraini Amruddin, Amru Yusrin |
| author_sort | Mokhtar, Muhammad Syahmi |
| collection | UPM |
| description | The uncontrollable spread of fake news through the net is irresistible in this globalization era. Fake news dissemination cannot be tolerated as the bad impacts of it to the society is really worrying. Furthermore, this will lead to more significant problems and potential threat such as confusion, misconceptions, slandering and luring users to share provocative lies made from fabricated news through their social media to occur. Within Malaysia context, there is lack in platform for fake news detection in Malay language articles and most of Malaysians received news through their social messaging applications. Fake news can be certainly solved by the aid of artificial intelligence which includes machine learning algorithms. The objective of this project is to propose a fake news detection model using Logistic Regression, to evaluate the performance of Logistic Regression as fake news detection model and to develop a web application that allows entry of a news content or news URL. In this study, Logistic Regression was applied in detecting fake news. Model development methodology is referenced and followed in this project. Based on existing studies, Logistic Regression showed a good performance in classification task. In addition, stance detection approach is added to improve the accuracy of the model performance. Based on analysis made, this model within stance detection approach yields an excellent accuracy using TF-IDF feature in constructing this fake news model. This model is then integrated with web service that accepts input either news URL or news content in text which is then checked for its truth level through “FAKEBUSTER” application. |
| first_indexed | 2024-03-06T10:26:42Z |
| format | Article |
| id | upm.eprints-79875 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T10:26:42Z |
| publishDate | 2019 |
| publisher | Blue Eyes Intelligence Engineering & Sciences Publication |
| record_format | dspace |
| spelling | upm.eprints-798752023-03-02T04:17:01Z http://psasir.upm.edu.my/id/eprint/79875/ Fakebuster: fake news detection system using logistic regression technique in machine learning Mokhtar, Muhammad Syahmi Jusoh, Yusmadi Yah Admodisastro, Novia Che Pa, Noraini Amruddin, Amru Yusrin The uncontrollable spread of fake news through the net is irresistible in this globalization era. Fake news dissemination cannot be tolerated as the bad impacts of it to the society is really worrying. Furthermore, this will lead to more significant problems and potential threat such as confusion, misconceptions, slandering and luring users to share provocative lies made from fabricated news through their social media to occur. Within Malaysia context, there is lack in platform for fake news detection in Malay language articles and most of Malaysians received news through their social messaging applications. Fake news can be certainly solved by the aid of artificial intelligence which includes machine learning algorithms. The objective of this project is to propose a fake news detection model using Logistic Regression, to evaluate the performance of Logistic Regression as fake news detection model and to develop a web application that allows entry of a news content or news URL. In this study, Logistic Regression was applied in detecting fake news. Model development methodology is referenced and followed in this project. Based on existing studies, Logistic Regression showed a good performance in classification task. In addition, stance detection approach is added to improve the accuracy of the model performance. Based on analysis made, this model within stance detection approach yields an excellent accuracy using TF-IDF feature in constructing this fake news model. This model is then integrated with web service that accepts input either news URL or news content in text which is then checked for its truth level through “FAKEBUSTER” application. Blue Eyes Intelligence Engineering & Sciences Publication 2019 Article PeerReviewed Mokhtar, Muhammad Syahmi and Jusoh, Yusmadi Yah and Admodisastro, Novia and Che Pa, Noraini and Amruddin, Amru Yusrin (2019) Fakebuster: fake news detection system using logistic regression technique in machine learning. International Journal of Engineering and Advanced Technology, 9 (1). pp. 2407-2410. ISSN 2249-8958 https://www.ijeat.org/portfolio-item/a2633109119/ 10.35940/ijeat.A2633.109119 |
| spellingShingle | Mokhtar, Muhammad Syahmi Jusoh, Yusmadi Yah Admodisastro, Novia Che Pa, Noraini Amruddin, Amru Yusrin Fakebuster: fake news detection system using logistic regression technique in machine learning |
| title | Fakebuster: fake news detection system using logistic regression technique in machine learning |
| title_full | Fakebuster: fake news detection system using logistic regression technique in machine learning |
| title_fullStr | Fakebuster: fake news detection system using logistic regression technique in machine learning |
| title_full_unstemmed | Fakebuster: fake news detection system using logistic regression technique in machine learning |
| title_short | Fakebuster: fake news detection system using logistic regression technique in machine learning |
| title_sort | fakebuster fake news detection system using logistic regression technique in machine learning |
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