Fact or Fake? How News Title, Sentiment and Writing Style help AI to detect COVID-19 Fake News?
This research presents a sophisticated model aimed at detecting COVID-19 related misinformation in Traditional Chinese, a critical response to the swift spread of fake news during the pandemic. The model employs an ensemble model of machine learning techniques, such as SVM, LSTM, BiLSTM, and BERT, a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2389502 |
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| author | Chen-Shu Wang Bo-Yi Li Kai-Wen Wang Zhi-Chi Lin |
| author_facet | Chen-Shu Wang Bo-Yi Li Kai-Wen Wang Zhi-Chi Lin |
| author_sort | Chen-Shu Wang |
| collection | DOAJ |
| description | This research presents a sophisticated model aimed at detecting COVID-19 related misinformation in Traditional Chinese, a critical response to the swift spread of fake news during the pandemic. The model employs an ensemble model of machine learning techniques, such as SVM, LSTM, BiLSTM, and BERT, along with a diverse array of input features including news structure, sentiment, and writing stylistic elements. Testing of the model has shown an impressive 97% accuracy in differentiating factual from fraudulent news. A significant finding is that in-depth content analysis offers more insights compared to mere headline scrutiny, though headlines do aid in marginally increasing accuracy. The integration of sentiment analysis and stylistic nuances further boosts the model’s effectiveness. This study is pivotal in establishing a robust Traditional-Chinese fake news detection mechanism for COVID-19, underscoring the effectiveness of combined machine learning strategies for more consistent and reliable outcomes. |
| format | Article |
| id | doaj-art-91acef5da3e44d15a19c3333f3f5acc2 |
| institution | DOAJ |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-91acef5da3e44d15a19c3333f3f5acc22025-08-20T02:49:22ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2389502Fact or Fake? How News Title, Sentiment and Writing Style help AI to detect COVID-19 Fake News?Chen-Shu Wang0Bo-Yi Li1Kai-Wen Wang2Zhi-Chi Lin3Department of Information and Finance Management, National Taipei University of Technology, Taipei, TaiwanDepartment of Management Information System, National Cheng-Chi University, Taipei, TaiwanGraduate Institute of Information and Computer Education, National Taiwan Normal University, Taipei, TaiwanDepartment of Information and Finance Management, National Taipei University of Technology, Taipei, TaiwanThis research presents a sophisticated model aimed at detecting COVID-19 related misinformation in Traditional Chinese, a critical response to the swift spread of fake news during the pandemic. The model employs an ensemble model of machine learning techniques, such as SVM, LSTM, BiLSTM, and BERT, along with a diverse array of input features including news structure, sentiment, and writing stylistic elements. Testing of the model has shown an impressive 97% accuracy in differentiating factual from fraudulent news. A significant finding is that in-depth content analysis offers more insights compared to mere headline scrutiny, though headlines do aid in marginally increasing accuracy. The integration of sentiment analysis and stylistic nuances further boosts the model’s effectiveness. This study is pivotal in establishing a robust Traditional-Chinese fake news detection mechanism for COVID-19, underscoring the effectiveness of combined machine learning strategies for more consistent and reliable outcomes.https://www.tandfonline.com/doi/10.1080/08839514.2024.2389502 |
| spellingShingle | Chen-Shu Wang Bo-Yi Li Kai-Wen Wang Zhi-Chi Lin Fact or Fake? How News Title, Sentiment and Writing Style help AI to detect COVID-19 Fake News? Applied Artificial Intelligence |
| title | Fact or Fake? How News Title, Sentiment and Writing Style help AI to detect COVID-19 Fake News? |
| title_full | Fact or Fake? How News Title, Sentiment and Writing Style help AI to detect COVID-19 Fake News? |
| title_fullStr | Fact or Fake? How News Title, Sentiment and Writing Style help AI to detect COVID-19 Fake News? |
| title_full_unstemmed | Fact or Fake? How News Title, Sentiment and Writing Style help AI to detect COVID-19 Fake News? |
| title_short | Fact or Fake? How News Title, Sentiment and Writing Style help AI to detect COVID-19 Fake News? |
| title_sort | fact or fake how news title sentiment and writing style help ai to detect covid 19 fake news |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2389502 |
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