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Characterizing the (Perceived) Newsworthiness of Health Science Articles: A Data-Driven Approach

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      Abstract

      BackgroundHealth science findings are primarily disseminated through manuscript publications. Information subsidies are used to communicate newsworthy findings to journalists in an effort to earn mass media coverage and further disseminate health science research to mass audiences. Journal editors and news journalists then select which news stories receive coverage and thus public attention.ObjectiveThis study aims to identify attributes of published health science articles that correlate with (1) journal editor issuance of press releases and (2) mainstream media coverage.MethodsWe constructed four novel datasets to identify factors that correlate with press release issuance and media coverage. These corpora include thousands of published articles, subsets of which received press release or mainstream media coverage. We used statistical machine learning methods to identify correlations between words in the science abstracts and press release issuance and media coverage. Further, we used a topic modeling-based machine learning approach to uncover latent topics predictive of the perceived newsworthiness of science articles.ResultsBoth press release issuance for, and media coverage of, health science articles are predictable from corresponding journal article content. For the former task, we achieved average areas under the curve (AUCs) of 0.666 (SD 0.019) and 0.882 (SD 0.018) on two separate datasets, comprising 3024 and 10,760 articles, respectively. For the latter task, models realized mean AUCs of 0.591 (SD 0.044) and 0.783 (SD 0.022) on two datasets—in this case containing 422 and 28,910 pairs, respectively. We reported most-predictive words and topics for press release or news coverage.ConclusionsWe have presented a novel data-driven characterization of content that renders health science “newsworthy.” The analysis provides new insights into the news coverage selection process. For example, it appears epidemiological papers concerning common behaviors (eg, alcohol consumption) tend to receive media attention.

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      Uses of expertise: sources, quotes, and voice in the reporting of genetics in the news

       Peter Conrad (1999)
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        Cancer information scanning and seeking behavior is associated with knowledge, lifestyle choices, and screening.

        Previous research on cancer information focused on active seeking, neglecting information gathered through routine media use or conversation ("scanning"). It is hypothesized that both scanning and active seeking influence knowledge, prevention, and screening decisions. This study uses Health Information National Trends Survey (HINTS, 2003) data to describe cancer-related scanning and seeking behavior (SSB) and assess its relationship with knowledge, lifestyle behavior, and screening. Scanning was operationalized as the amount of attention paid to health topics, and seeking was defined as looking for cancer information in the past year. The resulting typology included 41% low-scan/no-seekers; 30% high-scan/no-seekers; 10% low-scan/seekers, and 19% high-scan/seekers. Both scanning and seeking were significantly associated with knowledge about cancer (B=.36; B=.34) and lifestyle choices that may prevent cancer (B=.15; B=.16) in multivariate analyses. Both scanning and seeking were associated with colonoscopy (OR = 1.38, for scanning and OR=1.44, for seeking) and with prostate cancer screening (OR=4.53, scanning; OR=10.01, seeking). Scanning was significantly associated with recent mammography (OR=1.46), but seeking was not. Individuals who scan or seek cancer information are those who acquire knowledge, adopt healthy lifestyle behaviors, and get screened for cancer. Causal claims about these associations await further research.
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          Agenda Building, Source Selection, and Health News at Local Television Stations

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            Author and article information

            Affiliations
            1Department of Computer Science University of Texas at Austin Austin, TXUnited States
            2College of Media, Communication and Information University of Colorado Boulder Boulder, COUnited States
            3Department of Information Science University of Colorado Boulder Boulder, COUnited States
            4Biomedical Informatics Columbia University New York, NYUnited States
            5College of Computer and Information Science Northeastern University Boston, MAUnited States
            Author notes
            Corresponding Author: Ye Zhang yezhang1989@ 123456gmail.com
            Contributors
            , ORCID: http://orcid.org/0000-0002-0181-0975
            Journal
            JMIR Med Inform
            JMIR Med Inform
            JMI
            JMIR Medical Informatics
            JMIR Publications (Toronto, Canada )
            2291-9694
            Jul-Sep 2016
            22 September 2016
            : 4
            : 3
            27658571
            5054236
            v4i3e27
            10.2196/medinform.5353
            (Editor), (Reviewer), (Reviewer), (Reviewer), (Reviewer),
            ©Ye Zhang, Erin Willis, Michael J Paul, Noémie Elhadad, Byron C Wallace. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 22.09.2016.

            This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

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