Abstract
Computational news discovery (CND) is a particular application area within computational journalism related to the use of algorithms to orient editorial attention to potentially newsworthy events or information prior to publication. Previous work in this area has been concentrated on prototyping CND tools, which can, for instance, send alerts and leads to journalists about social media events, documents of interest, or salient patterns in streams of data. This article describes a qualitative interview study of journalists as they incorporate CND tools into their practices. Findings provide insights into how CND tools interact with the internal attention economy and sociotechnical gatekeeping processes of the newsroom and how future CND tools might better align with necessary journalistic evaluations of newsworthiness and quality, while ensuring configurability, human agency, and flexible applicability to a wide range of use cases. These findings begin to outline a conceptual framework that can help guide the effective design of future CND tools.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 945-967 |
| Number of pages | 23 |
| Journal | Digital Journalism |
| Volume | 8 |
| Issue number | 7 |
| DOIs | |
| State | Published - Aug 8 2020 |
Funding
This work was supported by the U.S. National Science Foundation under grant IIS-1845460. I would like to thank colleagues at Northwestern who offered helpful feedback on this article in the Research in Progress (RIP) seminar, and to the anonymous reviewers who pushed me to greatly improve the initial manuscript. I am also indebted to my interview participants for the time they spent speaking with me, thank you!
Keywords
- Algorithmic journalism
- automated journalism
- computational journalism
- computational news discovery
- data journalism
- news production
ASJC Scopus subject areas
- Communication