Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalism
Sachita Nishal, Jasmine Sinchai, Nicholas Diakopoulos
Abstract
Science and technology journalists today face challenges in finding newsworthy leads due to increased workloads, reduced resources, and expanding scientific publishing ecosystems. Given this context, we explore computational methods to aid these journalists' news discovery in terms of their agency and time-efficiency. We prototyped three computational information subsidies into an interactive tool that we used as a probe to better understand how such a tool may offer utility or more broadly shape the practices of professional science journalists. Our findings highlight central considerations around science journalists' user agency, contexts of use, and professional responsibility that such tools can influence and could account for in design. Based on this, we suggest design opportunities for enhancing and extending user agency over the longer-term; incorporating contextual, personal and collaborative notions of newsworthiness; and leveraging flexible interfaces and generative models. Overall, our findings contribute a richer view of the sociotechnical system around computational news discovery tools, and suggest ways to improve such tools to better support the practices of science journalists.