Towards Understanding and Supporting Journalistic Practices Using Semi-Automated News Discovery Tools

Nicholas DIakopoulos, Daniel Trielli, Grace Lee

Research output: Contribution to journalArticlepeer-review

Abstract

Journalists are routinely challenged with monitoring vast information environments in order to identify what is newsworthy and of interest to report to a wider audience. In a process referred to as computational news discovery, alerts and leads based on data-driven algorithmic analysis can orient journalists' attention to events, documents, or anomalous patterns in data that are more likely to be newsworthy. In this paper we prototype one such news discovery tool, Algorithm Tips, which we designed to help journalists find newsworthy leads about algorithmic decision-making systems used across all levels of U.S. government. The tool incorporates algorithmic, crowdsourced, and expert evaluations into an integrated interface designed to support users in making editorial decisions about which news leads to pursue. We then present an evaluation of our prototype based on an extended deployment with eight professional journalists. Our findings offer insights into journalistic practices that are enabled and transformed by such news discovery tools, and suggest opportunities for improving computational news discovery tool designs to better support those practices.

Original languageEnglish (US)
Article number406
JournalProceedings of the ACM on Human-Computer Interaction
Volume5
Issue numberCSCW2
DOIs
StatePublished - Oct 18 2021

Keywords

  • algorithmic accountability
  • computational journalism
  • computational news discovery
  • newsworthiness

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Human-Computer Interaction
  • Computer Networks and Communications

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