Module JG010

Computational Journalism
 

Module author

Tanni Haas

City University of New York
USA

Learning objectives

After studying this module, you will be able to:

  • Define and explain computational journalism;
  • Explain the reasons for this concept;
  • Give an overview of the historic development of computational journalism, including key persons who established this genre;
  • Reflect computational journalism critically.
Study point 1
Reading extract Computational Journalism

 

Why Open School of Journalism believes that Computational Journalism is important zu know

Computational journalism recognizes, quite simply, that times have changed. Classic journalism is gone. It has been replaced by a more complicated process with many obstacles to the truth. The biggest obstacles require a collaboration between journalists, software developers and social scientists. Computational journalism recognizes that new-gathering is hampered by technological shortfalls. This is a new field, not even ten years old, yet it is pushing forward fast as it looks for software solutions to problems that vex reporters of all kinds. Symposiums, such as a large one led by Georgia School for Technology, are designed to cross-pollinate between reporters and computer experts. The adherents of this idea hope that their efforts will maintain high standards of journalistic integrity while making the most of the new information technologies.

A brief history

Computational Journalism comes from an idea first taught in 2006. The originator was Professor Irfan Essa at Georgia Tech. This was later followed up by a large Georgia Tech symposium that gather hundreds of journalists and researchers. Across the country, Stanford University got involved, leading its own symposium and launching its own research into the idea. In 2009, Medill created a masters that emphasized the idea. Duke University launched the Reporter's Lab in 2011. This working group is aimed at creating software based on the needs of reporters. In 2012, Columbia Journalism School added "Frontiers of Computational Journalism" to its curriculum. At these and other schools across the country, more students are enrolled in dual degrees for journalism and computer science.

Classic journalism vs. Computational Journalism

Journalism schools have been struggling for years over how to deal with the changing media and the cluttered, yet vital information landscape. From smartphones to pads to laptops, information is at every person's fingertips. The need for speed, the difficulties of researching, and the muddied waters of the internet have changed the journalist's traditional methodology. Classic journalism has given way to computational journalism. The new curriculum is simply meant to recognize these changes and to meet the challenges head on.

Speed

Speed is one essential difference between classic and computational journalism. Before the internet, beat reporters faced deadlines. They had to tell the story in a few hours or by the next day. Now news stories are so immediate that facts must be told in real time, almost as quickly as they unfold. It's true even of investigative journalism pieces. Writers once took their time to put the facts together. Today's investigative reporters must work faster than ever before while the story is still relevant in the public's short attention span. Computational journalism recognizes this need for speed and seeks to maintain accuracy despite the challenges.

Research

Before the internet, reporters once worked hard to sift through newspapers, magazines books and public records. The internet has made the task look easier while actually making it much harder. There are more outlets for research, more ways to find information, and many more ways to come up against brick walls. It can waste time as much as it can help. Reporters, often desperate to make deadlines, are faced with difficult databases that aren't easy to search. Computational journalism recognizes that research is a major hurdle and seeks to find the tools that reporters need to do their job.

Active pursuit of better tools

Computational journalism is currently aimed at actively pursuing better tools for reporters. A team of Stanford and Duke researchers have proposed, for instance, a reporter's dashboard. This would provide journalists with a simple way to monitor a wide range of critical sources. It would help organize daily information, and it would help the reporter spot a critical fact that might go unnoticed in the clutter of internet activity. Reporters also need help with data mining. Sure, there are numerous public and private sources, but reporters need ways to sift through records that were developed to be searched. Then there's the unstructured or unreliable data that is often found. Finding viable programming solutions to these and other questions is key to computational journalism.

Furthering computational journalism

Many researchers have been working on this field in the short years since its inception. The efforts are often collaborative, overlapping as they do between journalism, computer science and the social sciences. New software has been developed that is meant to enhance computational journalism. TimeFlow was developed by Duke University in collaboration with Martin Wattenberg and Fernanda Viegas. This open-source program is a chronology tool that helps investigative journalists make sense of a lot of data quickly. It was used by Barron's when investigating Russian corruption and by ProPublica when investigating an Iraqi bombing's aftermath. Also developed by the Duke University lab, Haystax allows journalists to get information more quickly when searching public records databases. Recognizing the need for collaboration, Haystax was created by hackers working with reporters and community members in a 30-hour session. Video Notebook, yet another Duke-developed tool, allows for better, quicker analysis of video and audio sources.

The future 

Computational Journalism is an active idea that is seeking solutions to the problems that face today's reporters. At universities, it seeks to train both journalists and computer scientists to look for ways to make the system work. The hope is to maintain journalistic standards while making the most of computer capability.