A new framework for innovation in journalism: How a computer scientist would do it
Andrew Phelps writes:
What if journalism were invented today? How would a computer scientist go about building it, improving it, iterating it?
He might start by mapping out some fundamental questions: What are the project’s values and goals? What consumer needs would it satisfy? How much should be automated, how much human-powered? How could it be designed to be as efficient as possible?
Computer science Ph.D. Nick Diakopoulos has attempted to create a new framework for innovation in journalism. His new white paper, commissioned by CUNY’s Tow-Knight Center for Entrepreneurial Journalism, does not provide answers so much as a different way to come up with questions.
Diakopolous identified 27 computing concepts that could apply to journalism — think natural language processing, machine learning, game engines, virtual reality, information visualization — and pored over thousands of research papers to determine which topics get the most (and least) attention. (There are untapped opportunities in robotics, augmented reality, and motion capture, it turns out.)
He thinks computer science and journalism have a lot in common, actually. They are both fundamentally concerned with information. Acquiring it, storing it, modifying it, presenting it.
Suggest you read his paper in full: Cultivating the Landscape of Innovation in Computational Journalism.
Intrigued by the idea of gauging the opportunities along a continuum of activities. Could be a stunning visual of how subject identity is handled across activities and/or technologies.
Interested?
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