Application of unsupervised machine learning to identify and characterise hydroxychloroquine misinformation on Twitter.

Abstract

Authors

• Tim K Mackey
  Department of Anesthesiology, University of California, San Diego, School of Medicine, San Diego, CA, USA; Division of Global Public Health, University of California, San Diego, School of Medicine, Department of Medicine, San Diego, CA, USA; Global Health Policy Institute, San Diego, CA, USA. Electronic address: tmackey@ucsd.edu.
• Vidya Purushothaman
  Department of Family Medicine and Public Health, University of California, San Diego, San Diego, CA 92037, USA; Global Health Policy Institute, San Diego, CA, USA.
• Michael Haupt
  Department of Cognitive Science, University of California, San Diego, San Diego, CA 92037, USA.
• Matthew C Nali
  Department of Anesthesiology and Division of Infectious Disease and Global Public Health, University of California, San Diego, San Diego, CA 92037, USA; Global Health Policy Institute, San Diego, CA, USA; S-3 Research, San Diego, CA, USA.
• Jiawei Li
  School of Chemistry & Chemical Engineering, College of Guangling, Yangzhou University Yangzhou 225002 PR China zhuxiashi@sina.com.

Keywords

External Resources