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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A Practical Approach for Content Mining of Tweets</title><author><name sortKey="Yoon, Sunmoo" sort="Yoon, Sunmoo" uniqKey="Yoon S" first="Sunmoo" last="Yoon">Sunmoo Yoon</name></author><author><name sortKey="Elhadad, Noemie" sort="Elhadad, Noemie" uniqKey="Elhadad N" first="Noémie" last="Elhadad">Noémie Elhadad</name></author><author><name sortKey="Bakken, Suzanne" sort="Bakken, Suzanne" uniqKey="Bakken S" first="Suzanne" last="Bakken">Suzanne Bakken</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">23790998</idno><idno type="pmc">3694275</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694275</idno><idno type="RBID">PMC:3694275</idno><idno type="doi">10.1016/j.amepre.2013.02.025</idno><date when="2013">2013</date><idno type="wicri:Area/Pmc/Corpus">000455</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A Practical Approach for Content Mining of Tweets</title><author><name sortKey="Yoon, Sunmoo" sort="Yoon, Sunmoo" uniqKey="Yoon S" first="Sunmoo" last="Yoon">Sunmoo Yoon</name></author><author><name sortKey="Elhadad, Noemie" sort="Elhadad, Noemie" uniqKey="Elhadad N" first="Noémie" last="Elhadad">Noémie Elhadad</name></author><author><name sortKey="Bakken, Suzanne" sort="Bakken, Suzanne" uniqKey="Bakken S" first="Suzanne" last="Bakken">Suzanne Bakken</name></author></analytic><series><title level="j">American journal of preventive medicine</title><idno type="ISSN">0749-3797</idno><idno type="eISSN">1873-2607</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P2">Use of data generated through social media for health studies is gradually increasing. Twitter is a short-text message system developed 6 years ago, now with more than 100 million users generating over 300 million Tweets every day. Twitter may be used to gain real-world insights to promote healthy behaviors. The purposes of this paper are to describe a practical approach to analyzing Tweet contents and to illustrate an application of the approach to the topic of physical activity. The approach includes five steps: (1) selecting keywords to gather an initial set of Tweets to analyze; (2) importing data; (3) preparing data; (4) analyzing data (topic, sentiment, and ecologic context); and (5) interpreting data. The steps are implemented using tools that are publically available and free of charge and designed for use by researchers with limited programming skills. Content mining of Tweets can contribute to addressing challenges in health behavior research.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
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<front><journal-meta><journal-id journal-id-type="nlm-journal-id">8704773</journal-id><journal-id journal-id-type="pubmed-jr-id">1656</journal-id><journal-id journal-id-type="nlm-ta">Am J Prev Med</journal-id><journal-id journal-id-type="iso-abbrev">Am J Prev Med</journal-id><journal-title-group><journal-title>American journal of preventive medicine</journal-title></journal-title-group><issn pub-type="ppub">0749-3797</issn><issn pub-type="epub">1873-2607</issn></journal-meta><article-meta><article-id pub-id-type="pmid">23790998</article-id><article-id pub-id-type="pmc">3694275</article-id><article-id pub-id-type="doi">10.1016/j.amepre.2013.02.025</article-id><article-id pub-id-type="manuscript">NIHMS471565</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>A Practical Approach for Content Mining of Tweets</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Yoon</surname><given-names>Sunmoo</given-names></name><degrees>RN, PhD</degrees></contrib><contrib contrib-type="author"><name><surname>Elhadad</surname><given-names>Noémie</given-names></name><degrees>PhD</degrees></contrib><contrib contrib-type="author"><name><surname>Bakken</surname><given-names>Suzanne</given-names></name><degrees>RN, PhD</degrees></contrib><aff id="A1">School of Nursing (Yoon, Bakken), the Department of Biomedical Informatics (Yoon, Elhadad, Bakken), Columbia University, New York, New York</aff></contrib-group><author-notes><corresp id="FN1">Address correspondence to: Sunmoo Yoon, PhD, School of Nursing, Department of Biomedical Informatics, 630 West 168th Street, Mail Code 6, New York NY 10032. <email>sy2102@columbia.edu</email></corresp></author-notes><pub-date pub-type="nihms-submitted"><day>21</day><month>5</month><year>2013</year></pub-date><pub-date pub-type="ppub"><month>7</month><year>2013</year></pub-date><pub-date pub-type="pmc-release"><day>01</day><month>7</month><year>2014</year></pub-date><volume>45</volume><issue>1</issue><fpage>122</fpage><lpage>129</lpage><permissions><copyright-statement>© 2013 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.</copyright-statement><copyright-year>2013</copyright-year></permissions><abstract><p id="P2">Use of data generated through social media for health studies is gradually increasing. Twitter is a short-text message system developed 6 years ago, now with more than 100 million users generating over 300 million Tweets every day. Twitter may be used to gain real-world insights to promote healthy behaviors. The purposes of this paper are to describe a practical approach to analyzing Tweet contents and to illustrate an application of the approach to the topic of physical activity. The approach includes five steps: (1) selecting keywords to gather an initial set of Tweets to analyze; (2) importing data; (3) preparing data; (4) analyzing data (topic, sentiment, and ecologic context); and (5) interpreting data. The steps are implemented using tools that are publically available and free of charge and designed for use by researchers with limited programming skills. Content mining of Tweets can contribute to addressing challenges in health behavior research.</p></abstract><funding-group><award-group><funding-source country="United States">National Institute of Nursing Research : NINR</funding-source><award-id>T32 NR007969 || NR</award-id></award-group></funding-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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