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

Infodemiology and Infoveillance (1360) Peer-to-Peer Support and Online Communities (644) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1048) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1656)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45757, first published .
Staying Home, Tweeting Hope: Mixed Methods Study of Twitter Sentiment Geographical Index During US Stay-At-Home Orders

Staying Home, Tweeting Hope: Mixed Methods Study of Twitter Sentiment Geographical Index During US Stay-At-Home Orders

Staying Home, Tweeting Hope: Mixed Methods Study of Twitter Sentiment Geographical Index During US Stay-At-Home Orders

Authors of this article:

Xinming Xia1, 2, 3 Author Orcid Image ;   Yi Zhang4, 5 Author Orcid Image ;   Wenting Jiang6 Author Orcid Image ;   Connor Yuhao Wu6 Author Orcid Image
Article Authors Cited by (5) Tweetations (5) Metrics

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  2. Zhu Y, Wang J, Yuan Y, Meng B, Luo M, Shi C, Ji H. Spatial heterogeneities of residents' sentiments and their associations with urban functional areas during heat waves– a case study in Beijing. Computational Urban Science 2024;4(1) View
  3. Liu Z, Wu J, Wu C, Xia X. Shifting sentiments: analyzing public reaction to COVID-19 containment policies in Wuhan and Shanghai through Weibo data. Humanities and Social Sciences Communications 2024;11(1) View
  4. Wangpitipanit S, Piyatrakul S, Tongvichean T. Integrated Community-Based Care for Dependent Older People Community Participation in Preparation for Recurrent Outbreaks of COVID-19. Journal of Multidisciplinary Healthcare 2024;Volume 17:4519 View
  5. Jiang W, Zhang M, Wu C, Dong W. Rural‐Urban Differences in the Determinants of Subjective Well‐Being Among X/Twitter Users in the United States. Population, Space and Place 2025;31(1) View