A foundational concept for the new news economy

A journalist’s introduction to computational thinking

Kim Pearson, Department of English, Program in Interactive Multimedia, The College of New Jersey

Note:  A revised version of this essay has been published by the Poynter Institute’s E-Media Tidbits weblog.

I’m part of the post-Watergate generation of journalism school graduates, and right now I’m watching my peers struggle to master digital tools in an effort to stay relevant to an industry that is shifting ground under their feet. After years of working and collaborating with computer scientists at the forefront of the digital transformation of our culture, I’ve come to understand that what we need, most of all, is to master the fundamentals of what computer scientists have begun to identify as “computational thinking.” The good news is that there so many parallels between computational thinking and the ways of knowing that are embedded in the practice of journalism that one my collaborators, computer scientist Ursula Wolz argues that there is an “isomorphism,” or functional equivalence, between the two fields.

What is computational thinking?

It’s a way of reasoning — and a  way of defining problems, processes and relationships through which those problems are resolved. Jeannette Wing, a computer science professor at Carnegie Mellon University who also works at the National Science Foundation as Assistant Director for its Computer and Information Science and Engineering Directorate,  has argued that:

Computational thinking involves solving problems,
designing systems, and understanding human
behavior, by drawing on the concepts fundamental
to computer science. Computational thinking
includes a range of mental tools that reflect the
breadth of the field of computer science.

The website at the CMU Center for Computational Thinking elaborates concisely on Wing’s concept:

  • Computational thinking means creating and making use of different levels of abstraction, to understand and solve problems more effectively.
  • Computational thinking means thinking algorithmically and with the ability to apply mathematical concepts such as induction to develop more efficient, fair, and secure solutions.
  • Computational thinking means understanding the consequences of scale, not only for reasons of efficiency but also for economic and social reasons.

Computational thinking is more than digital literacy

Let’s begin with the obvious. Journalism had become a computing dependent profession long before the online revolution upended the business models that sustained the industry since the 1830s.  Investigative journalists, particularly, have been using government databases for decades. They have been creating databases since the early 1990s, and it’s no accident that many of the Pulitzer-prize winning stories over the last 15 years rely heavily on database reporting.

There’s no longer an argument about whether journalists need to be digitally literate. Today, newsgathering requires the ability to write programs that scrape public records databases and design interfaces that make the information in those databases interesting, relevant and accessible. It requires the programming and design skills to create interactive presentations that model complex public policy issues or explain social processes. It requires the mastery of social media technologies used to organize online communities around shared interests, issues and concerns.  It requires the ethical grounding needed to ensure that the content generated by these advanced tools is accurate, fair, comprehensive and proportional.

However, the digital transformation of newsgathering and delivery requires that journalists become creators, not just consumers of computing technologies.  I’m not saying that journalists need to become programmers.  I’m saying that we need to be able to reason abstractly about what we do,  understand the full pallette of computational tools at our disposal, and collaborate to deploy those tools with maximum efficiency and effectiveness.  That means understanding the underlying structures and processes of media creation.

What does that mean in practice?

Think about one of the basic functions of a local news operation: delivering occasional major breaking news bulletins. In the old days, an editor would tell a page make-up editor to tear up a front page to make space for a banner headline above the fold, along with a fast write-up of whatever information is available at the time, in inverted-pyramid style. There are rules – algorithms, if you will – that govern the entire process, from the fact that the headline has to contain a subject and predicate to the fact that there should be a dateline, and that sources should be authoritative and quotes should be pithy.

Now envision the same task in a modern newsroom. A programming-savvy editor will likely have worked with the site’s interactive editor to define a field within the site’s content management system called “Breaking News.” The most efficient policy would be to constrain headlines to 140 characters, and to have the RSS feed for the headlines linked to twitter via an API.  Similarly, the twitter feed should dump to a Facebook status message, as well as to SMS subscribers’ news alerts.   However, suppose the news site is a hyperlocal site without a full-time staff to actually develop the breaking news story.  Assuming that the site is a member of the Associated Press or a similarly credible pool service, the programming-savvy editor can create a function (or have one created) that will post an AP story that meets pre-defined criteria for a breaking news story to its content management system as a draft for approval, then alert the editor. After vetting the story, the editor can release the story as-is, or quickly get additional value-added content. The  editor’s knowledge of underlying computing structures and processes enhances the productivity and efficiency of the news operation.

Here are some additonal examples of how computational thinking is already changing the way we do journalism:

Traditional practice Practice informed by computational thinking
Getting news tips from sources Crowd-sourcing
Vetting information through multiple sources Not only vetting information through multiple sources, but also deconstructing the algorithms used to assign credibility to said sources
Text stories in inverted pyramid or narrative format Text stories “chunked” with lede grafs subheads and titles optimized for search engines.
Headline writing for clarity and reader engagement In addition, headlines are optimized for search engines and RSS readers
Spot news photos Interactive photo slide shows, perhaps with audio narration, that might allow panning, zooming, or remixing the content depending upon the editorial intent
Layout for news value, advertising placement Layout also based on requirements of multiple platforms, eyetracking, accessibility standards, microformats, and usability research
Information graphics Interactive, database driven information graphics segmented for easy, blogging, tagging, twittering, embedding or mashing-up
Investigative reporting and analysis: text images and other static, linear or tabular content Pulling aggregated, time-stamped geo-tagged data as part of the reporting process, creating or using social networks, user-generated content appropriately vetted and sourced, interactive information graphics development using an appropriate web-development framework, database structure, and user-centered interface design as part of the news presentation, text, audio, video (perhaps annotated and/or linked, still images
Editorial art Interactive web comics, games
Letters to the editor Comments, social media functions, APIs and other tools for community-building and reader engagement – need to balance editorial judgment with community-building needs

Best practices for computational journalism: a researchable question

Infusing computational thinking into journalism alters the epistemology of the field as fundamentally as the advent of objective reporting did 100 years ago.  Formal journalism education  emerged as part of the effort to codify and institutionalize the best practices of that day, and to serve a news industry oriented to an assembly-line based manufacturing culture.  A new journalism is emerging,  grounded in computational thinking, that mimics the values and processes of knowledge production in the information age — what some experts call remix culture.  (See Lessig , Navas, and Jenkins for more on that concept.)  As Clay Shirky has argued, that new journalism requires prolific experimentation to help us discover sustainable business models that will the civic functions of news.

Obviously, the marketplace will answer some of our questions. At the same time, scholars need to develop ethnographic models to help us understand these emerging news practices work and how they affect our culture. We need assessment models to help us understand how the creation and presentation of online and interactive news and information affect learning, civic participation and community cohesion. Some of this is happening, of course — witness the work of MIT’s Center for Future Civic Media, for example.  Our Interactive Journalism Institute for Middle Schoolers at The College of New Jersey, a National Science Foundation-funded demonstration project that uses interactive journalism to infuse computational thinking into the language arts curriculum, is another example.

This combination of marketplace experimentation and systematic documentation and reflection will yield a new set of best practices that will become the bedrock of journalism education in the future.  The actual tools that we use to implement those practices will continue to change.  However, if we educate ourselves properly, we can help to lead that change, ensuring that those evolving practices serve the best interests of democracy.

Posted in Computational Thinking, Journalism, journalism education, Teaching.

professorkim

My professional background is in public information, magazine journalism, blogging and journalism education. My current research is founded on the premise that democracy requires the broad participation of a computationally fluent citizenry. Civic media industries must reflect the communities they serve at the level of ownership, research and development, news gathering, presentation and community engagement. This adds greater urgency to the already critical need to broaden participation in computing. To that end, I have collaborated on curricular models for infusing computing into journalism education at both the scholastic and collegiate levels, and for promoting civic engagement in computer science education. My current interest is in exploring the potential of stochastic networks and as enhancement to social computing tools for broadening civic participation.
While most of this blog is devoted to my research in computational journalism and trends in journalism education, I occasionally do some storytelling of my own. This blog picks up where my other blogs, Professor Kim’s News Notes (http://professorkim.blogspot.com) and The Nancybelle Project (http://kimpearson.net/nancybelle.html) left off.

4 Comments

  1. Great chart. It goes into great detail with the comparisons between traditional and computational. With a computational approach, there will be a larger source for comments and quotes. Thanks to the internet, a reporter could speak to a broader range of people and have better access to inside information, comments, quotes and stories. The roles of a reader can change as they interact with the technology. They can jump from consumers to commenters or news originators (through blogging etc.)

    Overall, a great analysis on this trend. I’d love to read more about this in the future.

  2. Pingback: Why Computational Thinking Should be the Core of the New Journalism Mindset » Publish2 Blog

  3. Pingback: » Rethinking Our Thinking, part 2: Computational thinking and the new journalism mindset The Linchpen

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