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

JMIR Theme Issue: COVID-19 Special Issue (2394) Infodemiology and Infoveillance (1278) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1015) Outbreak and Pandemic Preparedness and Management (1679) Mass Media/Social Media Communication and Campaigns (450) Infectious Diseases (non-STD/STI) (1490) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1484) Health Care Quality and Health Services Research (252) Telehealth and Telemonitoring (1575)

Published on in Vol 23, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31101, first published .
Using Twitter Comments to Understand People’s Experiences of UK Health Care During the COVID-19 Pandemic: Thematic and Sentiment Analysis

Using Twitter Comments to Understand People’s Experiences of UK Health Care During the COVID-19 Pandemic: Thematic and Sentiment Analysis

Using Twitter Comments to Understand People’s Experiences of UK Health Care During the COVID-19 Pandemic: Thematic and Sentiment Analysis

Authors of this article:

Esther Ainley1 Author Orcid Image ;   Cara Witwicki1 Author Orcid Image ;   Amy Tallett1 Author Orcid Image ;   Chris Graham1 Author Orcid Image
Article Authors Cited by (14) Tweetations (14) Metrics

Journals

  1. Xavier T, Lambert J. Sentiment and emotion trends in nurses' tweets about the COVID‐19 pandemic. Journal of Nursing Scholarship 2022;54(5):613 View
  2. Stracqualursi L, Agati P. Tweet topics and sentiments relating to distance learning among Italian Twitter users. Scientific Reports 2022;12(1) View
  3. Alhuzali H, Zhang T, Ananiadou S. Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis. Journal of Medical Internet Research 2022;24(10):e40323 View
  4. Golder S, Jefferson L, McHugh E, Essex H, Heathcote C, Castro Avila A, Dale V, Van Der Feltz‐Cornelis C, Bloor K. General practitioners' wellbeing during the COVID‐19 pandemic: Novel methods with social media data. Health Information & Libraries Journal 2023;40(4):400 View
  5. Zeng Z, Deng Q, Liu W. Knowledge sharing of health technology among clinicians in integrated care system: The role of social networks. Frontiers in Psychology 2022;13 View
  6. Kastrati Z, Imran A, Daudpota S, Memon M, Kastrati M. Soaring Energy Prices: Understanding Public Engagement on Twitter Using Sentiment Analysis and Topic Modeling With Transformers. IEEE Access 2023;11:26541 View
  7. Thakur N. Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox. Big Data and Cognitive Computing 2023;7(2):116 View
  8. Fernández-Pichel M, Aragón M, Saborido-Patiño J, Losada D. Personality trait analysis during the COVID-19 pandemic: a comparative study on social media. Journal of Intelligent Information Systems 2024;62(1):117 View
  9. Sazon H, Catapan S, Rahimi A, Canfell O, Kelly J. How do Twitter users feel about telehealth? A mixed‐methods analysis of experiences, perceptions and expectations. Health Expectations 2024;27(1) View
  10. Mackintosh L, Ormandy P, Busby A, Hawkins J, Klare R, Silver C, Da Silva-Gane M, Santhakumaran S, Bristow P, Sharma S, Wellsted D, Chilcot J, Sridharan S, Steenkamp R, Harris T, Muirhead S, Lush V, Afuwape S, Farrington K. Impact of COVID-19 on patient experience of kidney care: a rapid review. Journal of Nephrology 2023;37(2):365 View
  11. Brassel S, Brunner M, Campbell A, Power E, Togher L. Exploring Discussions About Virtual Reality on Twitter to Inform Brain Injury Rehabilitation: Content and Network Analysis. Journal of Medical Internet Research 2024;26:e45168 View
  12. Stracqualursi L, Agati P. Twitter users perceptions of AI-based e-learning technologies. Scientific Reports 2024;14(1) View
  13. Kamba M, She W, Ferawati K, Wakamiya S, Aramaki E. Exploring the Impact of the COVID-19 Pandemic on Twitter in Japan: Qualitative Analysis of Disrupted Plans and Consequences. JMIR Infodemiology 2024;4:e49699 View
  14. Chakravarty U, Arifuzzaman S. Sentiment analysis of tweets on social security and medicare. Social Network Analysis and Mining 2024;14(1) View