Enhanced Multi-Class Detection of Fake News
The spread of fake news has emerged as a critical challenge in the digital era. Confusion and conflict can arise if people mistake fake news for real news. Thus, advanced detection methodologies are desired. This paper aims to identify fake news, while addressing the issue of class imbalances. We em...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2024-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/135581 |
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| author | Chih-yuan Li Soon Ae Chun James Geller |
| author_facet | Chih-yuan Li Soon Ae Chun James Geller |
| author_sort | Chih-yuan Li |
| collection | DOAJ |
| description | The spread of fake news has emerged as a critical challenge in the digital era. Confusion and conflict can arise if people mistake fake news for real news. Thus, advanced detection methodologies are desired. This paper aims to identify fake news, while addressing the issue of class imbalances. We employ multi-class fake news detection, an advanced methodology beyond traditional binary classification. We highlight CNN’s better performance over the baseline BERT model in the literature, with improvements in accuracy, precision, recall, and F1-Score. We uniquely experimented with four model variants: CNN and BERT with both trainable embeddings and BERT embeddings. Our experiment demonstrates CNN's effectiveness in identifying text patterns. To address class imbalances, we experimented with three different balancing methods. Our study includes fine-tuning ChatGPT for multi-class classification. The result indicates notable limitations in ChatGPT's automated classification, which highlights the complexities of AI-based categorization. Our findings demonstrate the CNN model's efficiency and effectiveness, and show the intricacies of fake news detection. These insights confirm the need for advanced AI methodologies in combating misleading information. |
| format | Article |
| id | doaj-art-a26c0cecfae44410a88134d334dcceb9 |
| institution | OA Journals |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-a26c0cecfae44410a88134d334dcceb92025-08-20T01:52:19ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622024-05-013710.32473/flairs.37.1.13558171960Enhanced Multi-Class Detection of Fake NewsChih-yuan Li0https://orcid.org/0000-0002-7983-634XSoon Ae Chun1James Geller2New Jersey Institute of TechnologyCity University of New York – College of Staten IslandNew Jersey Institute of TechnologyThe spread of fake news has emerged as a critical challenge in the digital era. Confusion and conflict can arise if people mistake fake news for real news. Thus, advanced detection methodologies are desired. This paper aims to identify fake news, while addressing the issue of class imbalances. We employ multi-class fake news detection, an advanced methodology beyond traditional binary classification. We highlight CNN’s better performance over the baseline BERT model in the literature, with improvements in accuracy, precision, recall, and F1-Score. We uniquely experimented with four model variants: CNN and BERT with both trainable embeddings and BERT embeddings. Our experiment demonstrates CNN's effectiveness in identifying text patterns. To address class imbalances, we experimented with three different balancing methods. Our study includes fine-tuning ChatGPT for multi-class classification. The result indicates notable limitations in ChatGPT's automated classification, which highlights the complexities of AI-based categorization. Our findings demonstrate the CNN model's efficiency and effectiveness, and show the intricacies of fake news detection. These insights confirm the need for advanced AI methodologies in combating misleading information.https://journals.flvc.org/FLAIRS/article/view/135581 |
| spellingShingle | Chih-yuan Li Soon Ae Chun James Geller Enhanced Multi-Class Detection of Fake News Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| title | Enhanced Multi-Class Detection of Fake News |
| title_full | Enhanced Multi-Class Detection of Fake News |
| title_fullStr | Enhanced Multi-Class Detection of Fake News |
| title_full_unstemmed | Enhanced Multi-Class Detection of Fake News |
| title_short | Enhanced Multi-Class Detection of Fake News |
| title_sort | enhanced multi class detection of fake news |
| url | https://journals.flvc.org/FLAIRS/article/view/135581 |
| work_keys_str_mv | AT chihyuanli enhancedmulticlassdetectionoffakenews AT soonaechun enhancedmulticlassdetectionoffakenews AT jamesgeller enhancedmulticlassdetectionoffakenews |