title: Characterizing the (Perceived) Newsworthiness of Health Science Articles: A Data-Driven Approach.
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.
authors.affiliation: Department of Computer Science, University of Texas at Austin, Austin, TX, United States. yezhang1989@gmail.com.
authors.forename: Ye
authors.identifier: http://orcid.org/0000-0002-0181-0975
authors.initials: Y
authors.lastname: Zhang
authors.forename: Erin
authors.identifier: http://orcid.org/0000-0002-1582-0867
authors.initials: E
authors.lastname: Willis
authors.forename: Michael J
authors.identifier: http://orcid.org/0000-0002-9149-7539
authors.initials: MJ
authors.lastname: Paul
authors.forename: Noémie
authors.identifier: http://orcid.org/0000-0001-9721-5240
authors.initials: N
authors.lastname: Elhadad
authors.forename: Byron C
authors.identifier: http://orcid.org/0000-0003-2409-7735
authors.initials: BC
authors.lastname: Wallace
keywords: media coverage; natural language processing; press release; text classification