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.

Highlights

  • More than 10 million people have been diagnosed with COVID-19 infection and 500,000 people have died as of June 29, 2020

  • We examine the sentiment of tweets about COVID19 related aspects such as social distancing and masks, by using Aspect-Based Sentiment Analysis (ABSA) based on domain specific aspect and opinion terms

  • Our work shows how Natural Language Processing (NLP) techniques could be applied to public health questions with domain expert involvement

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Summary

Introduction

More than 10 million people have been diagnosed with COVID-19 infection and 500,000 people have died as of June 29, 2020. Social media is an important source to learn about people’s reactions and concerns, and this information can be beneficial for public health institutes when designing interventions. Understanding both people’s reactions to COVID-19 and where their concerns lie helps to tailor public health strategy and create better informed interventions. We analyze COVID-19 related tweets using topic modeling and Aspect-Based Sentiment Analysis (ABSA) using human-in-theloop, and interpret the results with public health experts. We examine the sentiment of tweets about COVID19 related aspects such as social distancing and masks, by using ABSA based on domain specific aspect and opinion terms. Our work shows how Natural Language Processing (NLP) techniques could be applied to public health questions with domain expert involvement

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