COVID-19 Pandemic: An Overview of Machine and Deep Learning Methods for Analysis of Digital Media Texts Nouf Matar Alzahrani Albaha University, College of Computer Science and Information Technology, Saudi Arabia noufalzahrani@bu.edu.sa Abstract. The global efforts to generate information during the COVID-19 outbreak are awe-inspiring. Governments are attempting to make sure people’s health is safe and sound during this epidemic by automatically processing the giant amount of online text. This text helps governments to make appropriate policies promptly by understanding public opinion at a suitable time to avoid outrageous consequences. The social media platforms play a significant role in fostering healthy online public communities, particularly for the user to user interaction in such pandemic circumstances. However, manually processing and analyzing a huge amount of data is a troublesome task. So, the study aims to provide a comprehensive analysis of the methods used to automatically process and analyze the digital media text. Moreover, the study also sheds light on the traditional machine learning and deep learning algorithms used to monitor users’ activity, attitude, and response to ample amounts of information on various social media platforms during the outbreak of COVID-19. The study concludes that each digital platform has been used for different goals, such as communication and thus user’s response is different on each digital media platform. Keywords: social media, machine learning, deep learning, natural language processing. 1 Introduction The World Health Organization (WHO) officially announced the COVID-19 as the name of this new disease that poses a severe global threat 1 . A recent study [1] highlighted that worldwide hazards are unified, especially the case of the COVID-19 epidemic exhibits how the prevalence of information can be crucial in such pandemic situations. The flow of a huge amount of information, in particular, fake news on the internet can significantly augment the epidemic process because it influences people and fragments the social response [2]. 1 WHO: Naming the coronavirus disease (COVID-19) and the virus that causes it. 179 ISSN 1870-4069 Research in Computing Science 149(5), 2020 pp. 179–189; rec. 2020-04-13; acc. 2020-05-06