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

在当今世界，定义新闻行业的不仅是它所产生的新闻，而且是它在数字版图上产生的传播。传播最有可能的效果是更多的传播，这一法令最好被定义为一个命题，而不是一个科学原则，因为缺乏关于一个传播行为产生随后的多种传播活动的全部广度和深度的经验证据。新闻业是检验这一命题的独特环境，因为任何一条新闻的效用都取决于它在传播过程中所做的事情。虽然新闻机构跟踪和分析受众对其内容的即时反应，如观点、点赞和分享，但它们对完整的新闻生命周期相对较少了解，整个新闻生命周期由许多参与者出于不同意图启动的几个阶段组成。新闻媒体要与美国人民建立更强的信任，一个必要但不充分的条件是跟踪、分析和了解其新闻内容的传播生命周期，以便对其工作做出更明智的决定。该项目对新闻生命周期进行了大数据研究，这将为新闻机构提供一个重要工具，开始重新建立对美国人民的足够信任。在社会科学议程设置理论的推动下，来自计算机和数据科学的大数据方法将有助于跟踪本地新闻在网络上的传播生命周期。新闻业的生命周期通常涉及(1)新闻机构产生和传播原创新闻内容，(2)协助传播的数字平台(例如，新闻聚合器)，(3)分享内容的同行新闻机构，以及(4)受众反馈和传播。虽然到目前为止，这一领域的大多数研究都集中在国家新闻机构(如《纽约时报》)上，但我们认为，地方新闻是该行业重新确立其规范性民主价值的关键。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记者在环系统，能够跟踪当地新闻内容的生命周期，以跨时间和跨数字平台观察其使用和误用。拟议的系统将通过反应-意图分析和话题漂移确定当新闻的预期效果演变为积极的或消极的意外结果时的哪些阶段。新闻的意外负面传播影响包括触发不文明、两极分化的话语、受众误解、错误信息的产生和虚假叙述的永久化(例如，阴谋论)。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.