Automatically quantifying the scientific quality and sensationalism of news records mentioning pandemics: validating a maximum entropy machine-learning model.

Journal: Journal of clinical epidemiology

PMID: 26854419

Abstract

OBJECTIVE: To develop and validate a method for automatically quantifying the scientific quality and sensationalism of individual news records.

Authors

Steven J Hoffman

Global Strategy Lab, Faculty of Law, University of Ottawa, 57 Louis Pasteur St, Ottawa, Ontario, Canada K1N 6N5. Electronic address: steven.hoffman@uottawa.ca.
Victoria Justicz

Harvard Global Health Institute, Harvard University, 42 Church St, Cambridge, Massachusetts 02138, USA.

Keywords

Databases, Factual Humans Influenza A Virus, H1N1 Subtype Influenza, Human Information Dissemination Machine Learning Mass Media Pandemics Reproducibility of Results Sensitivity and Specificity Severe Acute Respiratory Syndrome

External Resources

View on PubMed Access via DOI PubMed (26854419)

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