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

Infodemiology and Infoveillance (1349) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Health Care Quality and Health Services Research (271)

Published on in Vol 19, No 5 (2017): May

Public Response to Obamacare on Twitter

Public Response to Obamacare on Twitter

Public Response to Obamacare on Twitter

Authors of this article:

Matthew A Davis1 Author Orcid Image ;   Kai Zheng2 Author Orcid Image ;   Yang Liu3 Author Orcid Image ;   Helen Levy4 Author Orcid Image
Article Authors Cited by (24) Tweetations (16) Metrics

Journals

  1. Park H, Park S, Chong M. Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea. Journal of Medical Internet Research 2020;22(5):e18897 View
  2. Oh H, Kim C, Jeon J. Public Sense of Water Fluoridation as Reflected on Twitter 2009–2017. Journal of Dental Research 2020;99(1):11 View
  3. Saleh S, Lehmann C, McDonald S, Basit M, Medford R. Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter. Infection Control & Hospital Epidemiology 2021;42(2):131 View
  4. Bian J, Zhao Y, Salloum R, Guo Y, Wang M, Prosperi M, Zhang H, Du X, Ramirez-Diaz L, He Z, Sun Y. Using Social Media Data to Understand the Impact of Promotional Information on Laypeople’s Discussions: A Case Study of Lynch Syndrome. Journal of Medical Internet Research 2017;19(12):e414 View
  5. van den Broek-Altenburg E, Atherly A. Using Social Media to Identify Consumers’ Sentiments towards Attributes of Health Insurance during Enrollment Season. Applied Sciences 2019;9(10):2035 View
  6. Ke Y, Taylor J, Gao L, Wang H, Zhao H, Byrne N, Modgil V, Butaney M, Makarov D, Prabhu V, Loeb S. Twitter response to the 2018 US Preventive Services Task Force guidelines on prostate cancer screening. BJU International 2019;124(3):363 View
  7. Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023 View
  8. He L, Yin T, Hu Z, Chen Y, Hanauer D, Zheng K. Developing a standardized protocol for computational sentiment analysis research using health-related social media data. Journal of the American Medical Informatics Association 2021;28(6):1125 View
  9. He L, He C, Reynolds T, Bai Q, Huang Y, Li C, Zheng K, Chen Y. Why do people oppose mask wearing? A comprehensive analysis of U.S. tweets during the COVID-19 pandemic. Journal of the American Medical Informatics Association 2021;28(7):1564 View
  10. Harrison C, Sidey-Gibbons C. Machine learning in medicine: a practical introduction to natural language processing. BMC Medical Research Methodology 2021;21(1) View
  11. Choo S, Lim E, Chang C, Li Y, Chang Y, Syed-Abdul S. How #TaiwanCanHelp Reverberates: An Exploratory Analysis of Advocacy Hashtag on Twitter. Social Media + Society 2022;8(3) View
  12. Feier J, Nguyen K, Choi J. Twitter Perspectives on Cochlear Implantation: Sentiment and Thematic Analysis. Otolaryngology–Head and Neck Surgery 2023;169(3):642 View
  13. Görtz C, Zorell C, Fitzgerald J. Casting light on citizens’ conceptions of what is ‘political’. Acta Politica 2023;58(1):57 View
  14. Dong X, Lian Y. A review of social media-based public opinion analyses: Challenges and recommendations. Technology in Society 2021;67:101724 View
  15. He L, Yin T, Zheng K. They May Not Work! An evaluation of eleven sentiment analysis tools on seven social media datasets. Journal of Biomedical Informatics 2022;132:104142 View
  16. Reveilhac M, Steinmetz S, Morselli D. A systematic literature review of how and whether social media data can complement traditional survey data to study public opinion. Multimedia Tools and Applications 2022;81(7):10107 View
  17. Ramadi K, Mehta R, He D, Chao S, Chu Z, Atun R, Nguyen F. Grass-roots entrepreneurship complements traditional top-down innovation in lung and breast cancer. npj Digital Medicine 2022;5(1) View
  18. Lanier H, Diaz M, Saleh S, Lehmann C, Medford R, De Silva D. Analyzing COVID-19 disinformation on Twitter using the hashtags #scamdemic and #plandemic: Retrospective study. PLOS ONE 2022;17(6):e0268409 View
  19. Ali S, Lowery C, Trude A. Leveraging Multiyear, Geospatial Social Media Data for Health Policy Evaluations: Lessons From the Philadelphia Beverage Tax. Journal of Public Health Management and Practice 2023;29(6):E253 View
  20. Diaz M, Medford R, Lehmann C, Petersen C. The lived experience of people with disabilities during the COVID-19 pandemic on Twitter: Content analysis. DIGITAL HEALTH 2023;9 View
  21. Lossio-Ventura J, Weger R, Lee A, Guinee E, Chung J, Atlas L, Linos E, Pereira F. A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data. JMIR Mental Health 2024;11:e50150 View
  22. Scott P. Conceptualizing health. International Journal of Language and Culture 2023;10(1):1 View
  23. Sorenson M, Houston R, Savage A. 280 Characters of Contention: Analyzing Partisan Behavior on Twitter During Supreme Court Confirmation Processes. Journal of Law and Courts 2024:1 View

Books/Policy Documents

  1. Chen N, Chen X, Pang J, Borga L, D’Ambrosio C, Vögele C. Social Informatics. View