Over the last few years, the journalism community has discussed mindset, skillset, journalist-programmers, and other ideas aimed not just at “saving journalism,” but making journalism better. Perhaps now it’s time to discuss how we think about journalism.
Greg Linch, the news innovation manager at Publish2, has been spreading an idea he calls “Rethinking Our Thinking.” The core of this idea is that journalists should explore other disciplines for concepts that they can use to do better journalism.
Linch begins this process by reading and writing about “computational thinking.” He asks, “What from the field of computation can we use to do better journalism?”
Jeannette Wing, a professor of computer science at Carnegie Mellon University, described computational thinking in the 2006 article that sparked Linch’s interest:
The three major areas that Wing outlines are automation, algorithms and abstraction.
Automation: How can we automate things that need to be done manually each time?
Good examples of automation applied to journalism include acquiring data through an API, aggregating links with Publish2 or even pushing RSS feeds through Twitter. Projects like StatSheet, Neighborhood Watch and NPRbackstory are good examples of automation in journalism.
Derek Willis recently wrote about how The New York Times uses APIs to “cut down on repetitive manual work and bring new ideas to fruition.”
The Times’ APIs make it easier to build applications and graphics that use some of the same information, such as “How G.O.P. Senators Plan to Vote on Kagan” and “Democrats to Watch on the Health Care Vote.”
Algorithms: How can we outline steps we should take to accomplish our goals, solve problems and find answers?
For example, journalists have a process for verifying facts through reporting. We ask sources for background information, sort through data, do our own research and conclude whether a statement is a fact.
A cops reporter’s call sheet is another algorithm: It’s a list of police and fire department phone numbers that the reporter is supposed to call at specified times to see whether there’s any news. Similarly, some news organizations have outlined processes on how to get background information on candidates, such as educational history, arrest records and business holdings.
Does your organization have a flowchart or a list for situations like these? Many reporters don’t like rules, but algorithms help make information-gathering more reliable and consistent.
Abstraction: At what different levels can we view this story or idea?
PolitiFact started out as a way to examine candidates’ claims during the 2008 presidential campaign. It now examines statements made in national politics, keeps track of President Obama’s campaign promises, and has branched out to cover politics in certain states. Earlier this year, PolitiFact teamed up with ABC’s “This Week” to fact-check guests on the show. PolitiFact could easily cover international politics as well.
In 2008, The New York Times built a document viewer to show Hillary Clinton’s past White House schedules. Programmers saw that the document viewer could be used for other stories, so they kept improving it.
Then a few people realized how the viewer could be part of a repository of documents, and DocumentCloud was born. The service builds on the Times’ code to create a space where journalists can share, search and analyze documents. DocumentCloud is an abstraction of the Times’ original document viewer.
Using computational thinking to improve corrections
Finally, an example in which all the aspects of computational thinking can make journalism better: corrections.
Scott Rosenberg of MediaBugs recently wrote about how badly news organizations handle corrections online. Rosenberg suggested some best practices for corrections: Make it easy for readers to report mistakes to you; review and respond to all error reports; make corrections forthright and accessible; make fixing mistakes a priority.
Some of these things can be automated. An online error report goes straight to someone who can manage it. Maybe the reader gets an automated “thank you” e-mail.
There could be an algorithm for investigating the error — or for fact-checking — and another algorithm to handle typos differently than factual errors.
Along the way, those readers who help your organization fix errors might become sources and contributors.
The point of “Rethinking Our Thinking,” Linch told me, is “not to try to fit things into the computational thinking box, but to consider the applications of computational thinking to improve the process of journalism.”
Perhaps we can apply methods of thinking used in other disciplines in the same way we apply “critical thinking” to journalism — less a conscious act and more a general awareness of concepts that can improve the practice.