Selective Feature Sets Based Fake News Detection for COVID-19 to Manage Infodemic
During the COVID-19 pandemic, the spread of fake news became easy due to the wide use of social media platforms. Considering the problematic consequences of fake news, efforts have been made for the timely detection of fake news using machine learning and deep learning models. Such works focus on mo...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9893055/ |
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| author | Manideep Narra Muhammad Umer Saima Sadiq Ala' Abdulmajid Eshmawi Hanen Karamti Abdullah Mohamed Imran Ashraf |
| author_facet | Manideep Narra Muhammad Umer Saima Sadiq Ala' Abdulmajid Eshmawi Hanen Karamti Abdullah Mohamed Imran Ashraf |
| author_sort | Manideep Narra |
| collection | DOAJ |
| description | During the COVID-19 pandemic, the spread of fake news became easy due to the wide use of social media platforms. Considering the problematic consequences of fake news, efforts have been made for the timely detection of fake news using machine learning and deep learning models. Such works focus on model optimization and feature engineering and the extraction part is under-explored area. Therefore, the primary objective of this study is to investigate the impact of features to obtain high performance. For this purpose, this study analyzes the impact of different subset feature selection techniques on the performance of models for fake news detection. Principal component analysis and Chi-square are investigated for feature selection using machine learning and pre-trained deep learning models. Additionally, the influence of different preprocessing steps is also analyzed regarding fake news detection. Results obtained from comprehensive experiments reveal that the extra tree classifier outperforms with a 0.9474 accuracy when trained on the combination of term frequency-inverse document frequency and bag of words features. Models tend to yield poor results if no preprocessing or partial processing is carried out. Convolutional neural network, long short term memory network, residual neural network (ResNet), and InceptionV3 show marginally lower performance than the extra tree classifier. Results reveal that using subset features also helps to achieve robustness for machine learning models. |
| format | Article |
| id | doaj-art-dc2fa4411cfe455492558e259b385f0b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-dc2fa4411cfe455492558e259b385f0b2025-08-20T03:42:15ZengIEEEIEEE Access2169-35362022-01-0110987249873610.1109/ACCESS.2022.32069639893055Selective Feature Sets Based Fake News Detection for COVID-19 to Manage InfodemicManideep Narra0https://orcid.org/0000-0001-7766-1710Muhammad Umer1https://orcid.org/0000-0002-6015-9326Saima Sadiq2https://orcid.org/0000-0002-2611-3738Ala' Abdulmajid Eshmawi3Hanen Karamti4Abdullah Mohamed5Imran Ashraf6https://orcid.org/0000-0002-8271-6496Indiana Institute of Technology, Fort Wayne, IN, USADepartment of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanDepartment of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi ArabiaResearch Centre, Future University in Egypt, New Cairo, EgyptDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaDuring the COVID-19 pandemic, the spread of fake news became easy due to the wide use of social media platforms. Considering the problematic consequences of fake news, efforts have been made for the timely detection of fake news using machine learning and deep learning models. Such works focus on model optimization and feature engineering and the extraction part is under-explored area. Therefore, the primary objective of this study is to investigate the impact of features to obtain high performance. For this purpose, this study analyzes the impact of different subset feature selection techniques on the performance of models for fake news detection. Principal component analysis and Chi-square are investigated for feature selection using machine learning and pre-trained deep learning models. Additionally, the influence of different preprocessing steps is also analyzed regarding fake news detection. Results obtained from comprehensive experiments reveal that the extra tree classifier outperforms with a 0.9474 accuracy when trained on the combination of term frequency-inverse document frequency and bag of words features. Models tend to yield poor results if no preprocessing or partial processing is carried out. Convolutional neural network, long short term memory network, residual neural network (ResNet), and InceptionV3 show marginally lower performance than the extra tree classifier. Results reveal that using subset features also helps to achieve robustness for machine learning models.https://ieeexplore.ieee.org/document/9893055/Fake news detectionResNetinceptionV3principal component analysis |
| spellingShingle | Manideep Narra Muhammad Umer Saima Sadiq Ala' Abdulmajid Eshmawi Hanen Karamti Abdullah Mohamed Imran Ashraf Selective Feature Sets Based Fake News Detection for COVID-19 to Manage Infodemic IEEE Access Fake news detection ResNet inceptionV3 principal component analysis |
| title | Selective Feature Sets Based Fake News Detection for COVID-19 to Manage Infodemic |
| title_full | Selective Feature Sets Based Fake News Detection for COVID-19 to Manage Infodemic |
| title_fullStr | Selective Feature Sets Based Fake News Detection for COVID-19 to Manage Infodemic |
| title_full_unstemmed | Selective Feature Sets Based Fake News Detection for COVID-19 to Manage Infodemic |
| title_short | Selective Feature Sets Based Fake News Detection for COVID-19 to Manage Infodemic |
| title_sort | selective feature sets based fake news detection for covid 19 to manage infodemic |
| topic | Fake news detection ResNet inceptionV3 principal component analysis |
| url | https://ieeexplore.ieee.org/document/9893055/ |
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