INTRODUCTION

During the COVID-19 pandemic, social media became increasingly relied upon for health information in Canada. By analyzing georeferenced tweets using natural language processing, we aimed to understand regional discussions and concerns about school closures, masking, vaccines, and lockdowns during the pandemic's first two years.

METHODS

Using Twitter's application programming interface, we collected English-language tweets with keywords related to COVID-19 posted between January 1, 2020 and February 22, 2022 from Canadian users.

RESULTS

Out of all retained tweets, 2,851,951 (47.9%) were about vaccines, 1,344,008 (22.6%) about lockdowns, 1,011,909 (17%) about schooling, and 752,014 (12.6%) about masking. Tweets on schooling received the most engagement, with the highest rates of likes (17.3%), retweets (18.7%), replies (10%), and quotes (6.8%). The most common emotions expressed were trust, fear, and anticipation, with lockdown tweets showing greater fear and sadness. Overall, sentiment was negative, particularly regarding lockdowns in the Northwest Territories and Alberta.

DISCUSSION

During the COVID-19 pandemic, Twitter became an essential tool for analyzing public sentiment regarding government actions. Users showed the most interest in vaccines, followed by lockdowns, schooling, and masking, with the highest engagement on schooling tweets. Our analysis of sentiment, emotion, and content revealed valuable insights into public beliefs about COVID-19 in Canada, highlighting regional differences and shifts in sentiment, particularly negative reactions to school closures as government recommendations evolved. Our study adds to the growing evidence supporting the use of natural language processing for real-time analysis of social media content to early identify public health concerns.