Exploratory Analysis of COVID-19 Related Tweets in North America to Inform Public Health Institutes

TL;DR

This study analyzes COVID-19 related tweets in North America, especially Canada, using NLP techniques to understand public reactions and concerns, providing insights for public health policy design.

Contribution

It demonstrates the application of NLP methods like topic modeling and sentiment analysis to public health social media data with expert interpretation.

Findings

Identification of key concerns expressed in tweets

Temporal correlation between public discussions and health interventions

Sentiment analysis revealing public mood trends

Abstract

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.