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

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...

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Detalles Bibliográficos
Autores principales: Zhang, Ye, Willis, Erin, Paul, Michael J, Elhadad, Noémie, Wallace, Byron C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054236/
https://www.ncbi.nlm.nih.gov/pubmed/27658571
http://dx.doi.org/10.2196/medinform.5353
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author Zhang, Ye
Willis, Erin
Paul, Michael J
Elhadad, Noémie
Wallace, Byron C
author_facet Zhang, Ye
Willis, Erin
Paul, Michael J
Elhadad, Noémie
Wallace, Byron C
author_sort Zhang, Ye
collection PubMed
description 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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spelling pubmed-50542362016-10-20 Characterizing the (Perceived) Newsworthiness of Health Science Articles: A Data-Driven Approach Zhang, Ye Willis, Erin Paul, Michael J Elhadad, Noémie Wallace, Byron C JMIR Med Inform Original Paper 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. JMIR Publications 2016-09-22 /pmc/articles/PMC5054236/ /pubmed/27658571 http://dx.doi.org/10.2196/medinform.5353 Text en ©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. https://creativecommons.org/licenses/by/2.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/ (https://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.
spellingShingle Original Paper
Zhang, Ye
Willis, Erin
Paul, Michael J
Elhadad, Noémie
Wallace, Byron C
Characterizing the (Perceived) Newsworthiness of Health Science Articles: A Data-Driven Approach
title Characterizing the (Perceived) Newsworthiness of Health Science Articles: A Data-Driven Approach
title_full Characterizing the (Perceived) Newsworthiness of Health Science Articles: A Data-Driven Approach
title_fullStr Characterizing the (Perceived) Newsworthiness of Health Science Articles: A Data-Driven Approach
title_full_unstemmed Characterizing the (Perceived) Newsworthiness of Health Science Articles: A Data-Driven Approach
title_short Characterizing the (Perceived) Newsworthiness of Health Science Articles: A Data-Driven Approach
title_sort characterizing the (perceived) newsworthiness of health science articles: a data-driven approach
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054236/
https://www.ncbi.nlm.nih.gov/pubmed/27658571
http://dx.doi.org/10.2196/medinform.5353
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