This paper is in the following e-collection/theme issue:

Infodemiology and Infoveillance (1047) JMIR Theme Issue: COVID-19 Special Issue (2168) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (863) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1323) Infectious Diseases (non-STD/STI) (1254) Mass Media/Social Media Communication and Campaigns (404) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1361) Outbreak and Pandemic Preparedness and Management (1492)

Published on in Vol 23 , No 2 (2021) :February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25431, first published .
Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis

Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis

Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis

Authors of this article:

Hyeju Jang 1, 2 Author Orcid Image ;   Emily Rempel 2 Author Orcid Image ;   David Roth 2 Author Orcid Image ;   Giuseppe Carenini 1 Author Orcid Image ;   Naveed Zafar Janjua 2, 3, 4 Author Orcid Image
Article Authors Cited by (63) Tweetations (9) Metrics

Hyeju Jang   1, 2 , MSc, PhD ;   Emily Rempel   2 , MSc, PhD ;   David Roth   2 , MSc, PhD ;   Giuseppe Carenini   1 , MSc, PhD ;   Naveed Zafar Janjua   2, 3, 4 , MBBS, MSc, DrPH

1 Department of Computer Science, University of British Columbia, Vancouver, BC, CA

2 British Columbia Centre for Disease Control, Vancouver, BC, CA

3 School of Population and Public Health, University of British Columbia, Vancouver, BC, CA

4 Centre for Health Evaluation and Outcome Sciences, University of British Columbia, Vancouver, BC, CA

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