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

Infodemiology and Infoveillance (1349) JMIR Theme Issue: COVID-19 Special Issue (2452) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Natural Language Processing (692) Machine Learning (1465) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42985, first published .
Examining Rural and Urban Sentiment Difference in COVID-19–Related Topics on Twitter: Word Embedding–Based Retrospective Study

Examining Rural and Urban Sentiment Difference in COVID-19–Related Topics on Twitter: Word Embedding–Based Retrospective Study

Examining Rural and Urban Sentiment Difference in COVID-19–Related Topics on Twitter: Word Embedding–Based Retrospective Study

Authors of this article:

Yongtai Liu1 Author Orcid Image ;   Zhijun Yin1, 2 Author Orcid Image ;   Congning Ni1 Author Orcid Image ;   Chao Yan2 Author Orcid Image ;   Zhiyu Wan2 Author Orcid Image ;   Bradley Malin1, 2, 3 Author Orcid Image
Article Authors Cited by (8) Tweetations (13) Metrics

Yongtai Liu   1 , PhD ;   Zhijun Yin   1, 2 , PhD ;   Congning Ni   1 , ME ;   Chao Yan   2 , PhD ;   Zhiyu Wan   2 , PhD ;   Bradley Malin   1, 2, 3 , PhD

1 Department of Computer Science, Vanderbilt University, Nashville, TN, United States

2 Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States

3 Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States

Corresponding Author: