文章基本信息

标题：A Deep Learning Framework for Detection of COVID-19 Fake News on Social Media Platforms
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作者：Yahya Tashtoush ; Balqis Alrababah ; Omar Darwish 等
期刊名称：Data
印刷版ISSN：2306-5729
出版年度：2022
卷号：7
期号：5
页码：1-17
DOI：10.3390/data7050065
语种：English
出版社：MDPI Publishing
摘要：The fast growth of technology in online communication and social media platforms allevi- ated numerous difﬁculties during the COVID-19 epidemic. However, it was utilized to propagate falsehoods and misleading information about the disease and the vaccination. In this study, we inves- tigate the ability of deep neural networks, namely, Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Network (CNN), and a hybrid of CNN and LSTM networks, to auto-matically classify and identify fake news content related to the COVID-19 pandemic posted on socialmedia platforms. These deep neural networks have been trained and tested using the “COVID-19 FakeNews” dataset, which contains 21,379 real and fake news instances for the COVID-19 pandemic and its vaccines. The real news data were collected from independent and internationally reliable institutions on the web, such as the World Health Organization (WHO), the International Committee of the Red Cross (ICRC), the United Nations (UN), the United Nations Children’s Fund (UNICEF), and their ofﬁcial accounts on Twitter. The fake news data were collected from different fact-checkingwebsites (such as Snopes, PolitiFact, and FactCheck). The evaluation results showed that the CNNmodel outperforms the other deep neural networks with the best accuracy of 94.2%.
关键词：text classiﬁcation;fake news detection;neural networks;deep learning;COVID-19;coronavirus;text mining