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COVID-19 Fake News Detector
2023 International Conference on Computing, Networking and Communications, ICNC 2023 ; : 463-467, 2023.
Article in English | Scopus | ID: covidwho-2298957
ABSTRACT
COVID-19 pandemic has been impacting people's everyday life for more than two years. With the fast spreading of online communication and social media platforms, the number of fake news related to COVID-19 is in a rapid growth and propagates misleading information to the public. To tackle this challenge and stop the spreading of fake news, this project proposes to build an online software detector specifically for COVID-19 news to classify whether the news is trustworthy. Specifically, as it is difficult to train a generic model for all domains, a base model is developed and fine-tuned to adapt the specific domain context. In addition, a data collection mechanism is developed to get latest COVID-19 news data and to keep the model fresh. We then conducted performance comparisons among different models using traditional machine learning techniques, ensemble machine learning, and the state-of-the-art deep learning mechanism. The most effective model is deployed to our online website for COVID-19 related fake news detection. © 2023 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2023 International Conference on Computing, Networking and Communications, ICNC 2023 Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2023 International Conference on Computing, Networking and Communications, ICNC 2023 Year: 2023 Document Type: Article