PreCheck: Understanding Press Release Exaggeration (PRE) of Scientific Research

PreCheck：了解科学研究的新闻稿夸大（PRE）

• 批准号: 1952353
• 负责人: Bei Yu
• 金额: $ 37.5万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1952353&HistoricalAwards=false
• 关键词: PreCheck; Understanding; Press; Release; Exaggeration

The accuracy of press releases plays a critical role in the public’s understanding of science and public trust in scientific research. However, several types of exaggeration have been found in press releases, such as making causal claims from correlational findings and inference to humans from animal studies. These exaggerations cause misinformation to be spread to the public through both social media and the mainstream media. This project furthers our understanding of how scholarly research findings may be miscommunicated to policymakers and the public by using state-of-the-art techniques in machine learning and artificial intelligence. This project provides an in-depth understanding at both micro- and macro-levels. On the micro-level, the project examines precisely when and where exaggeration occurs. On the macro-level, the project will examine the prevalence of exaggeration over time and across domains and institutions. These findings will provide useful insights for improving the quality of press releases as an important channel for science communication. This research will provide a large-scale, nuanced understanding of exaggerations in press releases issued by research institutions. The project develops a NLP and deep learning tool called PreCheck, which will automatically checking press release exaggeration, Specifically, PreCheck will link the press releases in EurekAlert!, the major academic press release portal, to the original research articles in PubMed, and then use machine learning and NLP techniques to examine each press release regarding two major types of exaggeration: causal claims of correlational findings and inference to humans from animal studies. Once exaggerations can be identified at the individual press release level, aggregated analyses will be done to answer research questions regarding the status and trend of press release exaggeration and its relationship with research areas and research institutions. The study results will be shared with press officers from various research institutions to discuss the research findings and their implications for communicating scientific research through press releases. In addition, PreCheck will be made publicly available so that science communicators can perform their own communication and science consumers can have a way to measure potential exaggeration in research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

新闻稿的准确性对于公众对科学的理解和公众对科学研究的信任起着至关重要的作用。然而，在新闻稿中发现了几种类型的夸大，例如从相关发现中提出因果关系，并从动物研究中推断人类。这些夸张导致错误信息通过社交媒体和主流媒体传播给公众。该项目进一步加深了我们对学术研究成果如何通过使用机器学习和人工智能中最先进的技术被错误传达给政策制定者和公众的理解。该项目提供了在微观和宏观层面的深入了解。在微观层面上，该项目精确地检查了夸张发生的时间和地点。在宏观层面上，该项目将审查随着时间的推移以及跨领域和跨机构的夸大现象的普遍性。这些发现将为提高新闻稿作为科学传播重要渠道的质量提供有益的启示。这项研究将对研究机构发布的新闻稿中的夸大内容提供大规模、细致入微的了解。该项目开发了一个名为PreCheck的NLP和深度学习工具，它将自动检查新闻稿夸大，具体来说，PreCheck将链接EurekAlert！主要的学术新闻发布门户网站，在PubMed上的原始研究文章，然后使用机器学习和NLP技术来检查每个新闻稿关于两种主要类型的夸张：相关发现的因果关系声明和从动物研究中对人类的推断。一旦可以识别出个别新闻稿层面的夸大，将进行汇总分析，以回答有关新闻稿夸大的现状和趋势及其与研究领域和研究机构的关系的研究问题。研究结果将与各研究机构的新闻官员分享，讨论研究结果及其对通过新闻稿传播科学研究的影响。此外，PreCheck还将向公众开放，以便科学传播者可以进行自己的传播，科学消费者可以有一种方法来衡量研究中潜在的夸大。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。