Automatically quantifying the scientific quality and sensationalism of news records mentioning pandemics: validating a maximum entropy machine-learning model
Identifieur interne : 001076 ( Main/Exploration ); précédent : 001075; suivant : 001077Automatically quantifying the scientific quality and sensationalism of news records mentioning pandemics: validating a maximum entropy machine-learning model
Auteurs : Steven J. Hoffman [Canada] ; Victoria Justicz [États-Unis]Source :
- Journal of Clinical Epidemiology [ 0895-4356 ] ; 2016.
Descripteurs français
- KwdFr :
- Apprentissage machine (normes), Bases de données factuelles, Diffusion de l'information (), Grippe humaine (épidémiologie), Humains, Mass-médias (normes), Pandémies, Reproductibilité des résultats, Sensibilité et spécificité, Sous-type H1N1 du virus de la grippe A, Syndrome respiratoire aigu sévère (épidémiologie).
- MESH :
English descriptors
- KwdEn :
- Databases, Factual, Humans, Influenza A Virus, H1N1 Subtype, Influenza, Human (epidemiology), Information Dissemination (methods), Machine Learning (standards), Mass Media (standards), Pandemics, Reproducibility of Results, Sensitivity and Specificity, Severe Acute Respiratory Syndrome (epidemiology).
- MESH :
- epidemiology : Influenza, Human, Severe Acute Respiratory Syndrome.
- methods : Information Dissemination.
- standards : Machine Learning, Mass Media.
- Databases, Factual, Humans, Influenza A Virus, H1N1 Subtype, Pandemics, Reproducibility of Results, Sensitivity and Specificity.
Abstract
To develop and validate a method for automatically quantifying the scientific quality and sensationalism of individual news records.
After retrieving 163,433 news records mentioning the Severe Acute Respiratory Syndrome (SARS) and H1N1 pandemics, a maximum entropy model for inductive machine learning was used to identify relationships among 500 randomly sampled news records that correlated with systematic human assessments of their scientific quality and sensationalism. These relationships were then computationally applied to automatically classify 10,000 additional randomly sampled news records. The model was validated by randomly sampling 200 records and comparing human assessments of them to the computer assessments.
The computer model correctly assessed the relevance of 86% of news records, the quality of 65% of records, and the sensationalism of 73% of records, as compared to human assessments. Overall, the scientific quality of SARS and H1N1 news media coverage had potentially important shortcomings, but coverage was not too sensationalizing. Coverage slightly improved between the two pandemics.
Automated methods can evaluate news records faster, cheaper, and possibly better than humans. The specific procedure implemented in this study can at the very least identify subsets of news records that are far more likely to have particular scientific and discursive qualities.
Url:
DOI: 10.1016/j.jclinepi.2015.12.010
PubMed: 26854419
PubMed Central: 7127105
Affiliations:
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Hoffman</name><affiliation wicri:level="1"><nlm:aff id="aff1">Global Strategy Lab, Faculty of Law, University of Ottawa, 57 Louis Pasteur St, Ottawa, Ontario, Canada K1N 6N5</nlm:aff><country>Canada</country><wicri:regionArea>Global Strategy Lab, Faculty of Law, University of Ottawa, 57 Louis Pasteur St, Ottawa, Ontario</wicri:regionArea><wicri:noRegion>Ontario</wicri:noRegion></affiliation></author><author><name sortKey="Justicz, Victoria" sort="Justicz, Victoria" uniqKey="Justicz V" first="Victoria" last="Justicz">Victoria Justicz</name><affiliation wicri:level="4"><nlm:aff id="aff2">Harvard Global Health Institute, Harvard University, 42 Church St, Cambridge, Massachusetts 02138, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Harvard Global Health Institute, Harvard University, 42 Church St, Cambridge, Massachusetts 02138</wicri:regionArea><orgName type="university">Université Harvard</orgName><placeName><settlement type="city">Cambridge (Massachusetts)</settlement><region type="state">Massachusetts</region></placeName></affiliation></author></analytic><series><title level="j">Journal of Clinical Epidemiology</title><idno type="ISSN">0895-4356</idno><idno type="eISSN">1878-5921</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Databases, Factual</term><term>Humans</term><term>Influenza A Virus, H1N1 Subtype</term><term>Influenza, Human (epidemiology)</term><term>Information Dissemination (methods)</term><term>Machine Learning (standards)</term><term>Mass Media (standards)</term><term>Pandemics</term><term>Reproducibility of Results</term><term>Sensitivity and Specificity</term><term>Severe Acute Respiratory Syndrome (epidemiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Apprentissage machine (normes)</term><term>Bases de données factuelles</term><term>Diffusion de l'information ()</term><term>Grippe humaine (épidémiologie)</term><term>Humains</term><term>Mass-médias (normes)</term><term>Pandémies</term><term>Reproductibilité des résultats</term><term>Sensibilité et spécificité</term><term>Sous-type H1N1 du virus de la grippe A</term><term>Syndrome respiratoire aigu sévère (épidémiologie)</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term><term>Severe Acute Respiratory Syndrome</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Information Dissemination</term></keywords><keywords scheme="MESH" qualifier="normes" xml:lang="fr"><term>Apprentissage machine</term><term>Mass-médias</term></keywords><keywords scheme="MESH" qualifier="standards" xml:lang="en"><term>Machine Learning</term><term>Mass Media</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term><term>Syndrome respiratoire aigu sévère</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Databases, Factual</term><term>Humans</term><term>Influenza A Virus, H1N1 Subtype</term><term>Pandemics</term><term>Reproducibility of Results</term><term>Sensitivity and Specificity</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Bases de données factuelles</term><term>Diffusion de l'information</term><term>Humains</term><term>Pandémies</term><term>Reproductibilité des résultats</term><term>Sensibilité et spécificité</term><term>Sous-type H1N1 du virus de la grippe A</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Objective</title><p>To develop and validate a method for automatically quantifying the scientific quality and sensationalism of individual news records.</p></sec><sec><title>Study design</title><p>After retrieving 163,433 news records mentioning the Severe Acute Respiratory Syndrome (SARS) and H1N1 pandemics, a maximum entropy model for inductive machine learning was used to identify relationships among 500 randomly sampled news records that correlated with systematic human assessments of their scientific quality and sensationalism. These relationships were then computationally applied to automatically classify 10,000 additional randomly sampled news records. The model was validated by randomly sampling 200 records and comparing human assessments of them to the computer assessments.</p></sec><sec><title>Results</title><p>The computer model correctly assessed the relevance of 86% of news records, the quality of 65% of records, and the sensationalism of 73% of records, as compared to human assessments. Overall, the scientific quality of SARS and H1N1 news media coverage had potentially important shortcomings, but coverage was not too sensationalizing. Coverage slightly improved between the two pandemics.</p></sec><sec><title>Conclusion</title><p>Automated methods can evaluate news records faster, cheaper, and possibly better than humans. 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Fleiss</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Landis, J R" uniqKey="Landis J">J.R. Landis</name></author><author><name sortKey="Koch, G G" uniqKey="Koch G">G.G. Koch</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hoffman, S J" uniqKey="Hoffman S">S.J. Hoffman</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Canada</li><li>États-Unis</li></country><region><li>Massachusetts</li></region><settlement><li>Cambridge (Massachusetts)</li></settlement><orgName><li>Université Harvard</li></orgName></list><tree><country name="Canada"><noRegion><name sortKey="Hoffman, Steven J" sort="Hoffman, Steven J" uniqKey="Hoffman S" first="Steven J." last="Hoffman">Steven J. Hoffman</name></noRegion></country><country name="États-Unis"><region name="Massachusetts"><name sortKey="Justicz, Victoria" sort="Justicz, Victoria" uniqKey="Justicz V" first="Victoria" last="Justicz">Victoria Justicz</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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