Nothing routine: Television news management's response to COVID-19, organizational uncertainty, and changes in news work

COVID-19 has impacted television news routines and created organizational challenges that required re-organization of journalism work (Wood, 2020). This shift created considerable uncertainty for news creation. Some of the journalistic professional values such as information gathering, interaction with sources, news judgment, information verification, proximity, human connections (Shoemaker & Reese, 2014) were applied in a different way. These shifts also affected news quality. Media sociology is the study of the forces that influence news content. This study focuses on Shoemaker and Reese’s (2014) hierarchical influence model primarily with regard to COVID’s impact at organizational and routine levels. The purpose of this study is to explore the virus’s impact on news work (organizational and routines), and management’s organizational responses toward journalism quality. The study includes in-depth interviews with broadcast news managers (news directors) in the southern Midwest of the U.S. (n = 13). The results of the study indicate the virus’s impact creates more horizontal (less hierarchical) and multilayered influences on news content. The pandemic is a macro-level influence and above the hierarchy of the influence model. It has hit everywhere. Yet, the data in this study suggest its influence on news is fluid, flowing up and down among organizational, routine, and individual levels

The News Crawler: A Big Data Approach to Local Information Ecosystems

In the past 20 years, Silicon Valley’s platforms and opaque algorithms have increasingly influenced civic discourse, helping Facebook, Twitter, and others extract and consolidate the revenues generated. That trend has reduced the profitability of local news organizations, but not the importance of locally created news reporting in residents’ day-to-day lives. The disruption of the economics and distribution of news has reduced, scattered, and diversified local news sources (digital-first newspapers, digital-only newsrooms, and television and radio broadcasters publishing online), making it difficult to inventory and understand the information health of communities, individually and in aggregate. Analysis of this national trend is often based on the geolocation of known news outlets as a proxy for community coverage. This measure does not accurately estimate the quality, scale, or diversity of topics provided to the community. This project is developing a scalable, semi-automated approach to describe digital news content along journalism-quality-focused standards. We propose identifying representative corpora and applying machine learning and natural language processing to estimate the extent to which news articles engage in multiple journalistic dimensions, including geographic relevancy, critical information needs, and equity of coverage