@inproceedings{jang-etal-2020-exploratory,
title = "Exploratory Analysis of {COVID}-19 Related Tweets in {N}orth {A}merica to Inform Public Health Institutes",
author = "Jang, Hyeju and
Rempel, Emily and
Carenini, Giuseppe and
Janjua, Naveed",
editor = "Verspoor, Karin and
Cohen, Kevin Bretonnel and
Conway, Michael and
de Bruijn, Berry and
Dredze, Mark and
Mihalcea, Rada and
Wallace, Byron",
booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
month = dec,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcovid19-2.18",
doi = "10.18653/v1/2020.nlpcovid19-2.18",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Exploratory Analysis of COVID-19 Related Tweets in North America to Inform Public Health Institutes
%A Jang, Hyeju
%A Rempel, Emily
%A Carenini, Giuseppe
%A Janjua, Naveed
%Y Verspoor, Karin
%Y Cohen, Kevin Bretonnel
%Y Conway, Michael
%Y de Bruijn, Berry
%Y Dredze, Mark
%Y Mihalcea, Rada
%Y Wallace, Byron
%S Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online
%F jang-etal-2020-exploratory
%X 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.
%R 10.18653/v1/2020.nlpcovid19-2.18
%U https://aclanthology.org/2020.nlpcovid19-2.18
%U https://doi.org/10.18653/v1/2020.nlpcovid19-2.18
Markdown (Informal)
[Exploratory Analysis of COVID-19 Related Tweets in North America to Inform Public Health Institutes](https://aclanthology.org/2020.nlpcovid19-2.18) (Jang et al., NLP-COVID19 2020)
ACL