Health, Psychosocial, and Social issues emanating from COVID- 19 pandemic based on Social Media Comments using Natural Language Processing Oladapo Oyebode1, Chinenye Ndulue1, Ashfaq Adib1, Dinesh Mulchandani1, Banuchitra Suruliraj1, Fidelia Anulika Orji4, Christine Chambers2, Sandra Meier3, and Rita Orji1 1 Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada 2 Department of Psychology and Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada 3 Department of Psychiatry, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada 4 Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada Corresponding Author: Oladapo Oyebode Faculty of Computer Science Dalhousie University Halifax, Nova Scotia, B3H 4R2 Canada Email:
[email protected] Abstract Background: The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioural change and policy initiatives, such as physical distancing, have been implemented to control the spread of the coronavirus. Social media data can reveal public perceptions toward how governments and health agencies across the globe are handling the pandemic, as well as the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally. Objective: This paper aims to investigate the impact of the COVID-19 pandemic on people globally using social media data. Methods: We apply natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collect over 47 million COVID-19- related comments from Twitter, Facebook, YouTube, and three online discussion forums.