A Deep Learning Framework for Detection of COVID-19 Fake News on Social Media Platforms

The fast growth of technology in online communication and social media platforms alleviated numerous difficulties during the COVID-19 epidemic. However, it was utilized to propagate falsehoods misleading information about disease vaccination. In this study, we investigate ability deep neural networks, namely, Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Network (CNN), a hybrid CNN LSTM automatically classify identify fake news content related pandemic posted on platforms. These networks have been trained tested using “COVID-19 Fake News” dataset, which contains 21,379 real instances for its vaccines. data were collected from independent internationally reliable institutions web, such as World Health Organization (WHO), International Committee Red Cross (ICRC), United Nations (UN), Children’s Fund (UNICEF), their official accounts Twitter. different fact-checking websites (such Snopes, PolitiFact, FactCheck). evaluation results showed that model outperforms other with best accuracy 94.2%.