NSF Convergence Accelerator Track F: America's Fourth Estate at Risk: A System for Mapping the (Local) Journalism Life Cycle to Rebuild the Nation's News Trust

NSF 融合加速器轨道 F：美国第四产业面临风险：绘制（本地）新闻生命周期图以重建国家新闻信任的系统

• 批准号: 2137846
• 负责人: Eduard Dragut
• 金额: $ 75万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2137846&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; America

In today’s world, the news industry is defined not only by the journalism it produces but by the resulting communication it engenders across the digital landscape. The edict that the most likely effect of communication is more communication is best defined as a proposition rather than a scientific principle due to a lack of empirical evidence concerning the full breadth and depth with which one act of communication produces a multitude of subsequent communicative engagements. Journalism is a unique setting to test this proposition in that the utility of any one piece of news is determined by what is done with it communicatively. While news organizations track and analyze immediate audience reactions to their content, such as views, likes, and shares, they have relatively little visibility and understanding of a complete news life cycle, which consists of several stages initiated by many actors with varied intentions. A necessary but not sufficient condition for the news media to build stronger levels of trust with the American people is to track, analyze, and understand the communication life cycle of their journalistic content to make more informed decisions about their work. This project undertakes a big data approach to the study of the news life cycle that will provide news organizations with an important tool to begin to re-establish sufficient levels of trust with the American people. A big data approach from computer and data science, driven by agenda-setting theory from the social sciences, will help track the communication life cycle of local news across the Web. The journalism life cycle typically involves (i) news organizations generating and disseminating original news content, (ii) digital platforms (e.g., news aggregators) aiding dissemination, (iii) fellow news organizations sharing content, and (iv) audience feedback and dissemination. While most research in this arena has so far focused on national news organizations (e.g., The New York Times), we argue that local news is key to the industry re-asserting its normative democratic value. By leveraging computational techniques like natural language processing and network analysis, the project’s primary goal is to develop a journalist-in-the-loop system able to track the life cycle of local journalistic content to observe its uses and misuses across time and across digital platforms. The proposed system will identify through reaction-intention analyses and topic drift those stages when journalism’s intended effects evolve into positive or negative unintended outcomes. Unintended, negative communication effects of news include the triggering of uncivil, polarizing discourse, audience misinterpretation, the production of misinformation, and the perpetuation of false narratives (e.g., conspiracy theories).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.

在当今的世界中，新闻业不仅是由它所产生的新闻界定义的，而且还由它在整个数字景观中产生的沟通来定义。最好的沟通效果是最好的沟通效果最好将其定义为一项建议，而不是科学原则，因为缺乏关于全部宽广和深度的经验证据，一种交流行为会产生多种随后的交流。新闻业是测试该提案的独特环境，因为任何一个新闻的效用都是由通信所做的事情决定的。尽管新闻机构跟踪和分析了对其内容的直接反应，例如观点，喜欢和分享，但他们对完整的新闻生命周期的知名度和理解相对较少，该新闻生命周期由许多有不同意图的演员发起的几个阶段组成。新闻媒体与美国人民建立更强大的信任水平的必要但不足的条件是跟踪，分析和理解其新闻内容的沟通生命周期，以做出有关其工作的更明智的决定。该项目对新闻生命周期的研究进行了大数据方法，该方法将为新闻机构提供一个重要的工具，以开始重新建立与美国人民的足够信任水平。来自社会科学的Agernda制定理论驱动的计算机和数据科学的大数据方法将有助于跟踪整个网络上本地新闻的通信生命周期。新闻生命周期通常涉及（i）新闻机构生成和传播原始新闻内容，（ii）数字平台（例如，新闻集合者）协助传播，（iii）共享内容的新闻机构以及（iv）受众的反馈和传播。尽管该领域的大多数研究都集中在国家新闻机构（例如《纽约时报》）上，但我们认为当地新闻是重新评估其正常民主价值的行业的关键。通过利用自然语言处理和网络分析等计算技术，该项目的主要目标是开发一个能够跟踪当地新闻内容生命周期的新闻工作者，以观察其在时间和跨数字平台之间的用途和错过。当新闻业的预期影响演变为正面或负面的意想不到的结果时，提出的系统将通过反应意图分析和主题进行这些阶段来识别这些阶段。新闻的意外，负面的沟通效果包括触发不文明，两极分化的话语，误解，错觉的产生以及对虚假叙述的持久性（例如，阴谋论）的持续性。这项奖项反映了NSF的法规使命，并认为通过基金会的知识优点和广泛的评论，可以通过评估来进行评估。