Stanford Computational Journalism Lab: Laying the foundations of US data journalism

Working from the ground up, Stanford University is taking a new approach to innovation to provide platforms for journalists to tell stories that would otherwise go untold.

From covering hackable voting machines to analysing police stops, Stanford are using data journalism to change public policy and raise awareness for the issues in America today. They aim to create real world change through their efforts, and have no intention of stopping.

The lab, founded in 2014, is one branch of the larger Stanford Journalism network, consisting of the Brown Institute for Media Innovation (a collaboration between Columbia Journalism School and the Stanford University’s School of Engineering) and The Stanford Daily Newspaper. Here they teach students how to extract and use data to enhance stories or uncover new stories. It is headed up by co-founder Cheryl Phillips but it is very much a fluid network of people who contribute. The lab’s mission is twofold: lower the cost of ‘accountability journalism’ and use computational methods to help uncover stories that would otherwise go untold.

Phillips has been teaching journalism at Stanford University since 2014. Having previously worked at the Seattle Times for more than a decade, with her most recent role being Data Innovation Editor, Phillips is no stranger to the world of media innovation:

“We want to help journalists tell stories in a more personalised and engaging way. That could be helping to build tools, to process data. We take care of the plumbing so the journalists can do the story.”

Big Local News

Phillips’ main focus currently lies with the Big Local News project where the goal is to develop partnerships across newsrooms to collect data, process it and make it available for journalists. Reporters can also save their data in the Stanford Digital Library Repository for as long as needed. This library is a digital space dedicated to making content accessible for future generations. In return for local journalists using this service, they get a persistent URL they can share out and don’t have to worry about their server changing or their system going down.

Additionally, Jonathan Stray, a journalist and lecturer from Columbia University, is working on a ‘computer journalism workbench’ (currently in beta), which is part of Big Local and it will be one of the tools used by journalists.

”The whole idea with that is to analyse data without having to code, so it’s built on Python (a programming language) but if you’re a journalist who doesn’t really know how to use that software you can use these modules to figure out how to analyse information,” says Phillips.

This work is that Phillips refers to as “doing the plumbing”, the kind of work that’s underground, not seen or thought about most of the time, but work which is absolutely essential to local news publishers and local journalists that are stretched for both time and money.

Structuring the Innovation Network

There are several different branches to the Stanford Journalism network including the Brown Institute and The Stanford Daily, so what does it look like? Phillips describes it as a “very fluid” structure, their seminars and workshops are taught by a variety of industry professionals ranging from the head of the Brown Institute, Maneesh Agrawala, to Krishna Bharat the founder of Google News. The department is free to investigate and flesh out different projects as they see fit, to create more conversations around topics, to foster collaborations and build partnerships throughout the industry.

This licence to follow their journalistic instinct has allowed them much more freedom when it comes to selecting projects. Phillips suggests they aren’t motivated by a need to generate revenue, but rather their focus is on impact in some form:

“What we’re going for is some kind of impact, changing policy if possible, just like with any kind of investigative journalism.”

But that is not the only driver. When someone comes to Phillips with a project or an idea she always tries to involve a newsroom if there is one interested. This not only gives those students a glimpse into a real newsroom but it helps them create a network and gain experience alongside real professionals.

There a few other key things Phillips look for in a project: is it new and does it have potential for change or reform? What is the minimum story that can be told? If all else fails is there still a story to be told? What could the maximum story be; just how far can this story go?

Mapping Hackable voting machines

This approach led to their work around “hackable” voting machines in Pennsylvania. The data journalism students investigated voting machines that were judged to be low tech and at risk of being hacked for each county in the state.

In ten weeks they mapped data for who had plans to upgrade their machines or not before the next voting cycle and published their findings in an article for the Philadelphia Inquirer. The results were that the county changed their policy on voting machines so that all counties, not just the four that planned to, updated their machines.

This is what this lab is aiming for; real world change as a result of doing these sorts of stories, building platforms so they don’t go untold and, maybe most importantly, to keep accountability journalism low cost and easy for reporters.

Problem solving through Innovation

On the broader topic of innovation Phillips says she looks to identify unmet needs and see how the structures or collaborations to fulfill those needs could be changed. They don’t however try to innovate from a standing start, says Phillips.

“I think it’s innovate off the back of [an issue] and solve a problem that’s kind of where our focus is. There’s so many stresses on local journalism and accountability journalism that there’s no shortage of problems. So we just try to tackle those where we can although sometimes I think the problems have as yet been unidentified.”

One of these many stresses on local journalism led to the introduction of the Big Local project:

“The idea is to collect local data sets that lend themselves to investigative or accountability journalism, then help local newsrooms with those and also aggregate data sets for bigger impact stories. People may have thought about doing something like this before, but they weren’t “running around saying ‘hey somebody has to do this’” Phillips added.

The Future

The media landscape is ever changing, but the Lab’s approach to innovation may be something that is quite consistent. Going forward, Phillips suggests that other universities looking to follow in their footsteps shouldn’t be afraid to make mistakes, rather to make them, learn and improve. As well as “just start teaching data journalism to your students”.

A study conducted by Phillips and Charles Berret, into journalism schools in the USA found that a little more than half regularly offered one or more data journalism class, but most of those classes were ‘basic’. The courses taught excel spreadsheets rather than programming skills and in 54 out of 113 cases, no data journalism course was taught.

The approach Cheryl Phillips takes to the future and what innovation may bring is something that anyone involved in the media innovation world should take on board:

“I think one of the things about newspapers in history, that you sometimes don’t get online, is that sense of serendipity, you might find a story you didn’t know you’d be interested in and then you read it…and it expands your horizons. Well the same thing is true of innovation.

I don’t know what they next tool might be. I hope I’m a part of it and the way I can make sure I’m going to be a part of it is to foster these conversations. Whether it’s a tool or an effort five years down the road I’m not sure what it’s all going to look like.

I have no doubt that the next time we have the computational journalism seminar, somebody will probably have an idea that won’t even be something I’ve thought of right and that’s that serendipity.Creating a ground for serendipitous moments that will help journalism.”


Cheryl Phillips, Co-Founder, Stanford Computational Journalism Lab
Twitter: @cephillips



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