The Future of News Work: Human-Technology Collaboration for Journalistic Research and Narrative Discovery

This project explores the future co-evolution of news work and computational tools. Journalists and news work are important to study because, even in the face of numerous challenges, news organizations remain a powerful force for keeping communities safe and healthy by providing information about events and trends to help people make better decisions about their lives. Journalism, as an information-based industry and profession, has always been impacted by new technologies, most recently by the rise of the Internet and loss of advertising revenue, as well as the long-standing tension between journalistic values and business interests. These pressures take a toll on journalistic processes and products. Some important stories are not getting told: they take too much time to discover, research, and craft into narratives, and time is increasingly in short supply. The project seeks to address these problems through a combination of fieldwork and user-centered development of interactive tools for supporting journalists in research and narrative discovery, i.e., finding the story that the data tell and interesting and relevant ways to convey that story to a reader.

This project will advance knowledge about technology and news work through field studies and user-centered development of interactive tools for supporting journalists in research and narrative discovery. The resulting technology-supported work processes will be evaluated in experiments with freelance journalists and through deployment in newsrooms. The proposed project is composed of several interwoven components, each with a specific focus, yet designed to be mutually supportive and to lead to research convergence: (1) focus on better understanding workers, their work and especially their current relationship to technology in newsrooms; (2) build and assess ways to support journalistic research, specifically finding relevant information in large document collections of different kinds, using deep learning techniques for entity extraction and coreference resolution (AllenNLP), semantic search (BERT) and common sense reasoning (COMET); and (3) build and assess ways to support narrative discovery. Resulting tools will be evaluated in field experiments and through their impacts on work examined in field studies. Both sets of tools will later be augmented by adding the ability to work on audio data. Assessment will be extended to consider alternative ways to divide tasks between human journalists and the computational tools. Running throughout the project will be work on evaluation, synthesis, dissemination of findings and tools, planning and project management.

The project is supported by an NSF grant, "The Future of News Work: Human-Technology Collaboration of Journalistic Research and Narrative Discovery", 21-29047 (Syracuse), 21-28906 (Stevens) and 21-29020 (Columbia). The PIs are Kevin Crowston & Keren Henderson (Syracuse), Jeffrey Nickerson (Stevens) and Lydia Chilton and Mark Hansen (Columbia).