Predicting News Coverage of Scientific Articles
Predicting News Coverage of Scientific Articles
复制标题
预测科学文章的新闻报道
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
David A. Smith
中科院分区：
文献类型：
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作者：
Ansel MacLaughlin;John P. Wihbey;David A. Smith
记者是科学界的守门人，控制着什么信息能进入公众的视线，以及如何呈现。分析通常受到更多媒体关注的研究类型对于理解诸如“科学传播科学”（美国国家科学院，工程和医学院2017），错误信息模式和“炒作周期”等问题至关重要。我们使用社交和传统媒体中领先的科学报道跟踪器Altmetric的元数据和文本数据，跟踪了2016年发表的91，997篇科学文章的覆盖范围，这些文章来自各个学科，出版商和新闻媒体。我们的方法是使用学习排名模型RumdaMART（布尔日2010），根据媒体关注度对每天或每周的报纸进行排名。我们发现标题、摘要和新闻稿的ngram特征显著提高了元数据特征期刊、出版商和主题的性能。
Journalists act as gatekeepers to the scientific world, controlling what information reaches the public eye and how it is presented. Analyzing the kinds of research that typically receive more media attention is vital to understanding issues such as the “science of science communication” (National Academies of Sciences, Engineering, and Medicine 2017), patterns of misinformation, and the “cycle of hype.” We track the coverage of 91,997 scientific articles published in 2016 across various disciplines, publishers, and news outlets using metadata and text data from a leading tracker of scientific coverage in social and traditional media, Altmetric. We approach the problem as one of ranking each day’s, or week’s, papers by their likely level of media attention, using the learning-to-rank model lambdaMART (Burges 2010). We find that ngram features from the title, abstract and press release significantly improve performance over the metadata features journal, publisher, and subjects.