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General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2014 Vol. 4(6): 496-500 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V6.461

A Text Mining Approach to Analyze Public Media Science Coverage and Public Interest in Science

Ying Sun
Abstract—The presentation of science in the mass media is one of the most important questions facing social scientists who analyze science. In this paper, we use topic modeling technique to identify the scientific topic areas or themes most prevalent in mass media over a given period of time to inform the discussion about civic scientific literacy (CSL). Google Trends is used to analyze public interests in science. The two sets of data are compared and correlated to identify any relationship between traditional media and the new media in impacting public perceptions of new scientific developments and public’s general understanding of science.

Index Terms—Data mining, civic science literacy, public interest in science, mass media.

Ying Sun is with University at Buffalo, USA (email: sun3@buffalo.edu).


Cite: Ying Sun, "A Text Mining Approach to Analyze Public Media Science Coverage and Public Interest in Science," International Journal of Machine Learning and Computing vol. 4, no. 6, pp. 496-500, 2014.

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