Title: Fake news and misinformation detection on headlines of COVID-19 using deep learning algorithms

Authors: Xin Wang; Peng Zhao; Xi Chen

Addresses: Big Data and AI Lab, IntelligentRabbit LLC, NJ 08852, USA ' Big Data and AI Lab, IntelligentRabbit LLC, NJ 08852, USA ' School of Humanity and Law, Beijing University of Civil Engineering and Architecture Beijing 100055, China

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.

Keywords: COVID-19; fake news detection; deep learning algorithms; big data analytics; mainstream media credibility; long short-term memory; convolutional neural network; deep belief networks; information retrieval; cloud computing.

DOI: 10.1504/IJDS.2020.115873

International Journal of Data Science, 2020 Vol.5 No.4, pp.316 - 332

Received: 05 Oct 2020
Accepted: 08 Feb 2021
Published online: 25 Jun 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article