Application of unsupervised machine learning to identify and characterise hydroxychloroquine misinformation on Twitter.
Identifieur interne : 000428 ( Main/Corpus ); précédent : 000427; suivant : 000429Application of unsupervised machine learning to identify and characterise hydroxychloroquine misinformation on Twitter.
Auteurs : Tim K. Mackey ; Vidya Purushothaman ; Michael Haupt ; Matthew C. Nali ; Jiawei LiSource :
- The Lancet. Digital health [ 2589-7500 ] ; 2021.
English descriptors
- KwdEn :
- MESH :
- chemical : Hydroxychloroquine.
- drug therapy : COVID-19.
- Communication, Humans, Internet Use, Machine Learning, Quackery, Social Media, Unsupervised Machine Learning.
DOI: 10.1016/S2589-7500(20)30318-6
PubMed: 33509386
Links to Exploration step
pubmed:33509386Le document en format XML
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<author><name sortKey="Mackey, Tim K" sort="Mackey, Tim K" uniqKey="Mackey T" first="Tim K" last="Mackey">Tim K. Mackey</name>
<affiliation><nlm:affiliation>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.</nlm:affiliation>
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<author><name sortKey="Purushothaman, Vidya" sort="Purushothaman, Vidya" uniqKey="Purushothaman V" first="Vidya" last="Purushothaman">Vidya Purushothaman</name>
<affiliation><nlm:affiliation>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.</nlm:affiliation>
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<author><name sortKey="Haupt, Michael" sort="Haupt, Michael" uniqKey="Haupt M" first="Michael" last="Haupt">Michael Haupt</name>
<affiliation><nlm:affiliation>Department of Cognitive Science, University of California, San Diego, San Diego, CA 92037, USA.</nlm:affiliation>
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<author><name sortKey="Nali, Matthew C" sort="Nali, Matthew C" uniqKey="Nali M" first="Matthew C" last="Nali">Matthew C. Nali</name>
<affiliation><nlm:affiliation>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.</nlm:affiliation>
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<author><name sortKey="Li, Jiawei" sort="Li, Jiawei" uniqKey="Li J" first="Jiawei" last="Li">Jiawei Li</name>
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<series><title level="j">The Lancet. Digital health</title>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>COVID-19 (drug therapy)</term>
<term>Communication (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Hydroxychloroquine (MeSH)</term>
<term>Internet Use (MeSH)</term>
<term>Machine Learning (MeSH)</term>
<term>Quackery (MeSH)</term>
<term>Social Media (MeSH)</term>
<term>Unsupervised Machine Learning (MeSH)</term>
</keywords>
<keywords scheme="MESH" type="chemical" xml:lang="en"><term>Hydroxychloroquine</term>
</keywords>
<keywords scheme="MESH" qualifier="drug therapy" xml:lang="en"><term>COVID-19</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Communication</term>
<term>Humans</term>
<term>Internet Use</term>
<term>Machine Learning</term>
<term>Quackery</term>
<term>Social Media</term>
<term>Unsupervised Machine Learning</term>
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<Title>The Lancet. Digital health</Title>
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<ArticleTitle>Application of unsupervised machine learning to identify and characterise hydroxychloroquine misinformation on Twitter.</ArticleTitle>
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