This paper presents an algorithm audit of the Google Top Stories box, a prominent component of search engine results and powerful driver of traffic to news publishers. As such, it is important in shaping user attention towards news outlets and topics. By analyzing the number of appearances of news article links we contribute a series of novel analyses that provide an in-depth characterization of news source diversity and its implications for attention via Google search. We present results indicating a considerable degree of source concentration (with variation among search terms), a slight exaggeration in the ideological skew of news in comparison to a baseline, and a quantification of how the presentation of items translates into traffic and attention for publishers. We contribute insights that underscore the power that Google wields in exposing users to diverse news information, and raise important questions and opportunities for future work on algorithmic news curation.

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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.

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