AngleKindling: Supporting Journalistic Angle Ideation with Large Language Models

要旨

News media often leverage documents to find ideas for stories, while being critical of the frames and narratives present. Developing angles from a document such as a press release is a cognitively taxing process, in which journalists critically examine the implicit meaning of its claims. Informed by interviews with journalists, we developed AngleKindling, an interactive tool which employs the common sense reasoning of large language models to help journalists explore angles for reporting on a press release. In a study with 12 professional journalists, we show that participants found AngleKindling significantly more helpful and less mentally demanding to use for brainstorming ideas, compared to a prior journalistic angle ideation tool. AngleKindling helped journalists deeply engage with the press release and recognize angles that were useful for multiple types of stories. From our findings, we discuss how to help journalists customize and identify promising angles, and extending AngleKindling to other knowledge-work domains.

著者
Savvas Petridis
Columbia University, New York, New York, United States
Nicholas Diakopoulos
Northwestern University, Evanston, Illinois, United States
Kevin Crowston
Syracuse University, Syracuse, New York, United States
Mark Hansen
Columbia University, New York, New York, United States
Keren Henderson
Syracuse University, Syracuse, New York, United States
Stan Jastrzebski
Syracuse University, Syracuse, New York, United States
Jeffrey V. Nickerson
Stevens Institute of Technology, Hoboken, New Jersey, United States
Lydia B. Chilton
Columbia University, New York, New York, United States
論文URL

https://doi.org/10.1145/3544548.3580907

動画

会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

セッション: Education and Support

Hall G1
6 件の発表
2023-04-26 20:10:00
2023-04-26 21:35:00