TY - JOUR
T1 - Towards Understanding and Supporting Journalistic Practices Using Semi-Automated News Discovery Tools
AU - DIakopoulos, Nicholas
AU - Trielli, Daniel
AU - Lee, Grace
N1 - Funding Information:
This work is supported by the National Science Foundation via award IIS-1845460, and the North-western University Undergraduate Research Assistant Program (URAP). We thank Johnathan Smith for building the front-end of the system. This work wouldn’t have been possible without the journalists who volunteered their time to use our system and participate in the research, thank you!
Publisher Copyright:
© 2021 ACM.
PY - 2021/10/18
Y1 - 2021/10/18
N2 - 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.
AB - 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.
KW - algorithmic accountability
KW - computational journalism
KW - computational news discovery
KW - newsworthiness
UR - http://www.scopus.com/inward/record.url?scp=85117929259&partnerID=8YFLogxK
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U2 - 10.1145/3479550
DO - 10.1145/3479550
M3 - Article
AN - SCOPUS:85117929259
VL - 5
JO - Proceedings of the ACM on Human-Computer Interaction
JF - Proceedings of the ACM on Human-Computer Interaction
SN - 2573-0142
IS - CSCW2
M1 - 406
ER -