For the last several months I’ve been working on a whitepaper for the CUNY Tow-Knight Center for Entrepreneurial Journalism. It’s about cultivating more technical innovation in journalism and involves systematically mapping out what’s been done (in terms of research) as well as outlining a method for people to generate new ideas in computational journalism. I’m happy to say that the paper was published by the Tow-Knight Center today. You can get Jeff Jarvis’ take on it on the Tow-Knight blog, or for more coverage you can see the Nieman Lab write-up. Or go straight for the paper itself.
About
I'm a Tow Fellow at the Columbia University Journalism School working on applications of data and computational journalism. I'm also a consultant specializing in research, design, and development for computational media applications. Areas of expertise include data visualization, social computing, and news. Find me on Twitter: @ndiakopoulosRecent Posts
- Diversity in the Robot Reporter Newsroom
- The Anatomy of a Robot Journalist
- OpenVis is for Journalists!
- Computational Journalism and The Reporting of Algorithms
- Making Data More Familiar with Concrete Scales
- Storytelling with Data Visualization: Context is King
- Algorithmic Defamation: The Case of the Shameless Autocomplete
- Sex, Violence, and Autocomplete Algorithms: Methods and Context
- The Rhetoric of Data
- Data on the Growth of CitiBike
- 51% Foreign: Algorithms and the Surveillance State
- To Save Everything, Deliberate it Endlessly?
- Storytelling with Data: What Are the Impacts on the Audience?
- How does newspaper circulation relate to Twitter following?
- Neolithic Journalists? Influence Engines? Narrative Analytics? Some Thoughts on C+J
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