This project-based course explores the use of data science, information visualization, artificial intelligence and emerging technologies to help journalists discover and tell stories, understand their audience, advance free speech and build trust. Students form interdisciplinary teams to tackle some of the most urgent challenges facing journalism.
The course is taught as a team by Computer Science Professor Maneesh Agrawala, director of the Brown Institute for Media Innovation at Stanford; Serdar Tumgoren, a former data journalist at The Associated Press and associate director of the Big Local News project at Stanford; and Steve Henn, an Entrepreneur in Residence at the Brown Institute for Media Innovation at Stanford University.
In Winter Quarter 2022, the course will meet on Tuesday mornings. Admission is by application; we invite students from any academic discipline to apply. Applications will be considered through Jan. 7, 2022.
What is Computational Journalism? Interactions among journalists, software developers, computer scientists and scholars will continue to evolve the answer to that question in the years ahead. For now, though, we define it as “the combination of algorithms, data, and knowledge from the social sciences to supplement the accountability function of journalism.” — “Accountability through Algorithm: Developing the Field of Computational Journalism”
Another dimension focuses on “changing how stories are discovered, presented, aggregated, monetized, and archived.” — “Computational Journalism”
Although computational journalism builds on and incorporates elements of computer-assisted reporting and data journalism, the evolving field often involves larger data sets and more sophisticated algorithms. Recent advances center on reporting by algorithms, about algorithms, and through algorithms.
Check out the below video to learn more about the course from students and instructors.