Citation

Huang SH, Tsao SF, Chen H, Bin Noon G, Li L, Yang Y, Butt ZA. Int. J. Public Health 2022; 67: e1605241.

Copyright

(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group)

DOI

10.3389/ijph.2022.1605241

PMID

36387289

PMCID

PMC9649435

Abstract

OBJECTIVES: This study aimed to investigate public discourses and sentiments regarding the Freedom Convoy in Canada on Twitter.

METHODS: English tweets were retrieved from Twitter API from 15 January to 14 February 2022 when the Freedom Convoy occurred. Unsupervised topic modelling and sentiment analysis were applied to identify topics and sentiments for each topic.

RESULTS: Five topics resulted from the topic modelling, including convoy support, political arguments toward the current prime minister, lifting vaccine mandates, police activities, and convoy fundraising. Overall, sentiments for each topic began with more positive or negative sentiments but approached to neutral over time.

CONCLUSION: The results show that sentiments towards the Freedom Convoy generally tended to be positive. Five topics were identified from the data collected, and these topics highly correlated with the events of the convoy. Our study also demonstrated that a mixed approach of unsupervised machine learning techniques and manual validation could generate timely evidence.

Language: en

Keywords

COVID-19; sentiment analysis; topic modeling; twitter data; vaccine mandate