applied sciences  
A Novel Hybrid Deep Learning Model for Detecting COVID-19-Related Rumors on Social Media Based on LSTM and Concatenated Parallel CNNs  
[PERSON] 1,2,*(c), [PERSON] 1,*(c), [PERSON] 1,3,*(c), [PERSON] 1,4(c) and [PERSON] 1  
College of Computer Science and Engineering, Taibah University, Medina 41477, Saudi Arabia; [EMAIL_ADDRESS] (W.B. ); [EMAIL_ADDRESS] (O.A.) Information System Department, Saba'a Region University, Manek, Yemen 1  
the competent health authorities recommended a set of preventive and precautionary measures originating from their keenness to protect people's health and ensure their safety. Based on the World Health Organization's (WHO) statistics, even with such stringent precautionary measures adopted by the kingdom, the number of infections in Saudi Arabia was doubling every 6 days. However, this exponential growth in the total number of infected cases with COVID-19 has been reported by several countries. Therefore, with the exponential growth of the number of infected people and the rapid spread of the virus in different countries, users with different professions as well as laypeople can post and circulate any related news. This piece of medical news might cause a panic situation in a population as well as market disorder. To avoid this situation, competent health authorities and other government authorities issued several rules to criminalize posting and sharing any fake information on social media. To solve this issue, there is a need to propose an intelligent way to detect health-related COVID-19 information automatically on social media. Although a few intelligent methods have been used to detect rumors on social media, obtaining a high detection rate is still a large challenge, especially if the rumors are written in languages such as Arabic. One of the main online social networking platforms is Twitter, which is currently used by several health and governmental authorities as one of the main sources of information and for announcing various information and awareness about emergency situations and regulations issued. Despite these efforts, it is noted that Twitter has become a breeding ground for several COVID-19 rumors. Thus, a broad number of researchers from different scientific fields such as computer science, social science, and psychology sought the reasons behind spreading rumors on social media platforms and for a way to combat rumors at the early stages [3]. Detecting rumors on social media platforms as a classification task has been addressed in several studies such as [3; 4; 5; 6; 7; 8; 9; 10]. By analyzing the literature, the used techniques for detecting rumors can be easily categorized either as traditional machine learning-based (ML) or deep learning-based DL methods. The ML approaches require several preprocessing and feature engineering processes. Oppositely, DL methods can extract the informative features directly from the textual content without human assistance. Among these studies, only few researchers have investigated the detection of rumors that are written in Arabic on social media. The detection rate of the existing methods still needs to be improved. Because of this research problem, this paper proposes a deep learning based model to detect COVID-19-related rumors posted on Twitter using the Arabic language. We investigated different deep learning architectures using the publicly available dataset ArCOV-19 [11]. The proposed deep learning model connects long short-term memory (LSTM) with three parallel and concatenated conventional neural networks (PCNN). The experimental results showed that the proposed model outperformed all the other investigated models and produced satisfied results in terms of accuracy, recall, precision, and F-score. The main contributions of this paper are:  
* A new LSTM-PCNN architecture is proposed and extensive experiments are presented to demonstrate the performance of the proposed model. * The impact of word embedding layers is investigated in order to select the appropriate scheme.