Stanford Panelists Explore “Computational Journalism”
On December 3, the Brown Institute for Media Innovation and the Knight Journalism Fellowship Program hosted the “Computation, Journalism, and the Future of News” panel at Stanford. The panel featured Stanford professor of Computer Science Jure Leskovec, founder of Google News Krishna Bharat, Jennifer LaFleur of ProPublica and the Center for Investigative Reporting, and James Hamilton, director of Stanford’s journalism program. Ann Grimes, associate director of the Brown Institute, moderated the conversation. To a room full of journalists and techies, panelists spoke on what they believe to be the pros and cons of the budding marriage between data and journalism.
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Definitions of “computational journalism” ranged from “knowing how to create a spreadsheet in Excel” to “processing and analyzing terabytes of information” to “the creation of new products to revolutionize journalism.”
As technology is allowing journalists to do things they could never before do, Leskovec advocates teaching journalists how to code, even if just at a base level. “It’s like learning to write,” he commented, “but everyone doesn’t have to be a poet.”
Bharat voiced one key difference between technologists and journalists that needs resolution for computational journalism to succeed: Techies understand that the mission stays the same though the method or process may not, while journalists champion a set process and mission. Bharat believes that computational journalism will make the difference between “reporting what everyone else can report and reporting something valuable and unique.”
Hamilton believes computational journalism is a way to lower the cost of telling stories and to help tell stories differently.
“Five years from now, how will we know this marriage has worked,” Grimes questioned. For Leskovec, when he sees a screen that is more than blank with a few letters, the marriage will have succeeded. LaFleur remarked that, for the marriage to last, journalists must learn to do something new: to “love and hug data.”
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