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

Hyeju Jang, Emily Rempel, Giuseppe Carenini, Naveed Janjua


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.
Anthology ID:
2020.nlpcovid19-2.18
Volume:
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
Month:
December
Year:
2020
Address:
Online
Editors:
Karin Verspoor, Kevin Bretonnel Cohen, Michael Conway, Berry de Bruijn, Mark Dredze, Rada Mihalcea, Byron Wallace
Venue:
NLP-COVID19
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
Language:
URL:
https://aclanthology.org/2020.nlpcovid19-2.18
DOI:
10.18653/v1/2020.nlpcovid19-2.18
Bibkey:
Cite (ACL):
Hyeju Jang, Emily Rempel, Giuseppe Carenini, and Naveed Janjua. 2020. Exploratory Analysis of COVID-19 Related Tweets in North America to Inform Public Health Institutes. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.
Cite (Informal):
Exploratory Analysis of COVID-19 Related Tweets in North America to Inform Public Health Institutes (Jang et al., NLP-COVID19 2020)
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PDF:
https://aclanthology.org/2020.nlpcovid19-2.18.pdf
Video:
 https://slideslive.com/38939848