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

Infodemiology and Infoveillance (709) JMIR Theme Issue: COVID-19 Special Issue (1347) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (562) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1022) Infectious Diseases (non-STD/STI) (928) Mass Media/Social Media Communication and Campaigns (309) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (982) Outbreak and Pandemic Preparedness and Management (1039)

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 (8) Tweetations (8) 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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