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

      JMIR Medical Informatics
      JMIR Publications
      natural language processing, text classification, press release, media coverage

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          Abstract

          Background Health 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. Objective This 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. Methods We 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. Results Both 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. Conclusions We 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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          Most cited references43

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

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              Health Attitudes, Health Cognitions, and Health Behaviors among Internet Health Information Seekers: Population-Based Survey

              Background Using a functional theory of media use, this paper examines the process of health-information seeking in different domains of Internet use. Objective Based on an analysis of the 1999 HealthStyles data, this study was designed to demonstrate that people who gather information on the Internet are more health-oriented than non-users of Internet health information. Methods The Porter Novelli HealthStyles database, collected annually since 1995, is based on the results of nationally representative postal mail surveys. In 1999, 2636 respondents provided usable data for the HealthStyles database. Independent sample t-tests and logistic regression analyses were conducted. Results The results showed that individuals who searched for health information on the Internet were indeed more likely to be health-oriented than those who did not. Consumers who sought out medical information on the Internet reported higher levels of health-information orientation and healthy activities, as well as stronger health beliefs than those who did not search for medical news on the Internet. It was observed that those who reported searching for information about drugs and medications on the Internet held stronger health beliefs than the non-searchers. Comparison of individuals who reported seeking out information about specific diseases on the Internet with individuals who did not showed those who sought out disease-specific information on the Internet to be more health-oriented. Finally, consumers who sought out healthy lifestyle information on the Internet were more health conscious and more health-information oriented than those who did not. They were also more likely to hold stronger health-oriented beliefs and to engage in healthy activities. Conclusions The results support the functional theory of Internet use. Internet searchers who used the Internet for a wide range of health purposes were typically more health oriented than non-searchers.
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                Author and article information

                Journal
                27658571
                5054236
                10.2196/medinform.5353
                http://creativecommons.org/licenses/by/2.0/

                natural language processing,text classification,press release,media coverage

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