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

Autor: Mackey TK; Department of Anesthesiology and Division of Infectious Disease and Global Public Health, University of California, San Diego, San Diego, CA 92037, USA; Department of Healthcare Research and Policy, 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. Electronic address: tmackey@ucsd.edu., Purushothaman V; 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., Haupt M; Department of Cognitive Science, University of California, San Diego, San Diego, CA 92037, USA., Nali MC; 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., Li J; Department of Healthcare Research and Policy, 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.
Jazyk: angličtina
Zdroj: The Lancet. Digital health [Lancet Digit Health] 2021 Feb; Vol. 3 (2), pp. e72-e75.
DOI: 10.1016/S2589-7500(20)30318-6
Databáze: MEDLINE