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

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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.