Fake news and misinformation detection on headlines of COVID-19 using deep learning algorithms
by Xin Wang; Peng Zhao; Xi Chen
International Journal of Data Science (IJDS), Vol. 5, No. 4, 2020

Abstract: This paper proposed a deep learning algorithm system to fulfil fake news and misinformation detection on COVID-19 related headlines. Long short-term memory (LSTM), convolutional neural network (CNN) and Deep belief networks (DBNs) are performed in order to determine the optimal algorithm. Based on the model performance measures, such as accuracy, AUC score, and F1 score, this study figures out the optimal models, which are CNN and LSTM with an accuracy of up to 94%, for the COVID-19 fake news detection. Finally, this paper provides an algorithm-based ranking method for mainstream media credibilities. The result indicates that mainstream media channels in the US are reliable for reporting the COVID-19 related news and information.

Online publication date: Fri, 25-Jun-2021

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