Exploratory Analysis of COVID-19 Related Tweets in North America to Inform Public Health Institutes
This work provides insights for public health agencies in North America, but it is incremental as it applies standard NLP methods to a new dataset.
The researchers analyzed COVID-19 related tweets in North America using topic modeling and sentiment analysis to understand public reactions and concerns, with results interpreted by public health experts to inform policy design.
Social media is a rich source where we can learn about people's reactions to social issues. As COVID-19 has significantly impacted on people's lives, it is essential to capture how people react to public health interventions and understand their concerns. In this paper, we aim to investigate people's reactions and concerns about COVID-19 in North America, especially focusing on Canada. We analyze COVID-19 related tweets using topic modeling and aspect-based sentiment analysis, and interpret the results with public health experts. We compare timeline of topics discussed with timing of implementation of public health interventions for COVID-19. We also examine people's sentiment about COVID-19 related issues. We discuss how the results can be helpful for public health agencies when designing a policy for new interventions. Our work shows how Natural Language Processing (NLP) techniques could be applied to public health questions with domain expert involvement.