ABSTRACT
The COVID-19 pandemic has affected people's lives in many ways. Social media data can reveal public perceptions and experience with respect to the pandemic, and also reveal factors that hamper or support efforts to curb global spread of the disease. In this paper, we analyzed COVID-19-related comments collected from six social media platforms using natural language processing (NLP) techniques. We identified relevant opinionated keyphrases and their respective sentiment polarity (negative or positive) from over 1 million randomly selected comments, and then categorized them into broader themes using thematic analysis. Our results uncover 34 negative themes out of which 17 are economic, socio-political, educational, and political issues. Twenty (20) positive themes were also identified. We discuss the negative issues and suggest interventions to tackle them based on the positive themes and research evidence.
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 behavioral change and policy initiatives such as physical distancing have been implemented to control the spread of COVID-19. Social media data can reveal public perceptions toward how governments and health agencies worldwide are handling the pandemic, and 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 worldwide using social media data. METHODS: We applied 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 collected over 47 million COVID-19-related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we performed data preprocessing, which involved applying NLP techniques to clean and prepare the data for automated key phrase extraction. Third, we applied the NLP approach to extract meaningful key phrases from over 1 million randomly selected comments and computed sentiment score for each key phrase and assigned sentiment polarity (ie, positive, negative, or neutral) based on the score using a lexicon-based technique. Fourth, we grouped related negative and positive key phrases into categories or broad themes. RESULTS: A total of 34 negative themes emerged, out of which 15 were health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues were increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues were frustrations due to life disruptions, panic shopping, and expression of fear. Social issues were harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes were public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research. CONCLUSIONS: We uncovered various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommended interventions that can help address the health, psychosocial, and social issues based on the positive themes and other research evidence. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, and in reacting to any future pandemics.