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

Health and Risk Communication (45) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1038) JMIR Theme Issue: COVID-19 Special Issue (2450) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Outbreak and Pandemic Preparedness and Management (1721) Infectious Diseases (non-STD/STI) (1567) Mass Media/Social Media Communication and Campaigns (461) Infodemiology and Infoveillance (1346)

Published on in Vol 2, No 1 (2022): Jan-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31473, first published .
The Evolution of Public Sentiments During the COVID-19 Pandemic: Case Comparisons of India, Singapore, South Korea, the United Kingdom, and the United States

The Evolution of Public Sentiments During the COVID-19 Pandemic: Case Comparisons of India, Singapore, South Korea, the United Kingdom, and the United States

The Evolution of Public Sentiments During the COVID-19 Pandemic: Case Comparisons of India, Singapore, South Korea, the United Kingdom, and the United States

Authors of this article:

May O Lwin1 Author Orcid Image ;   Anita Sheldenkar1 Author Orcid Image ;   Jiahui Lu2 Author Orcid Image ;   Peter Johannes Schulz3 Author Orcid Image ;   Wonsun Shin4 Author Orcid Image ;   Chitra Panchapakesan1 Author Orcid Image ;   Raj Kumar Gupta5 Author Orcid Image ;   Yinping Yang5 Author Orcid Image
Article Authors Cited by (10) Tweetations (3) Metrics

Journals

  1. Ainapure B, Pise R, Reddy P, Appasani B, Srinivasulu A, Khan M, Bizon N. Sentiment Analysis of COVID-19 Tweets Using Deep Learning and Lexicon-Based Approaches. Sustainability 2023;15(3):2573 View
  2. Zhou B, Miao R, Jiang D, Zhang L. Can people hear others’ crying?: A computational analysis of help-seeking on Weibo during COVID-19 outbreak in China. Information Processing & Management 2022;59(5):102997 View
  3. Stevens H, Rasul M, Oh Y. Emotions and Incivility in Vaccine Mandate Discourse: Natural Language Processing Insights. JMIR Infodemiology 2022;2(2):e37635 View
  4. Koh L, Ng C, Wang X, Yuen K. Social media engagement in the maritime industry during the pandemic. Technological Forecasting and Social Change 2023;192:122553 View
  5. Ansell L, Dalla Valle L. A new data integration framework for Covid-19 social media information. Scientific Reports 2023;13(1) View
  6. Lande J, Pillay A, Chandra R, Alam M. Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicron. PLOS ONE 2023;18(8):e0288681 View
  7. Lwin M, Yang S, Sheldenkar A, Yang X, Lee B. Assessing consumer rationality during a pandemic: Panic buying behaviours and its association with online social media discourse. Computers in Human Behavior Reports 2024;13:100361 View
  8. Costantini H, Costantini R, Fuse R. Changing Health Information on COVID-19 Vaccination in Asia. Journalism and Media 2024;5(2):526 View
  9. Othman N, Panchapakesan C, Loh S, Zhang M, Gupta R, Martanto W, Phang Y, Morris R, Loke W, Tan K, Subramaniam M, Yang Y. Predicting public mental health needs in a crisis using social media indicators: a Singapore big data study. Scientific Reports 2024;14(1) View
  10. Liu M, Yuan S, Li B, Zhang Y, Liu J, Guan C, Chen Q, Ruan J, Xie L. Chinese Public Attitudes and Opinions on Health Policies During Public Health Emergencies: Sentiment and Topic Analysis. Journal of Medical Internet Research 2024;26:e58518 View