What is Computational Journalism?
Computational journalism is an evolving field. Definitions include:
“What is computational journalism? Ultimately, interactions among journalists, software developers, computer scientists and other scholars over the next few years will have to answer that question. For now though, we define computational journalism as the combination of algorithms, data, and knowledge from the social sciences to supplement the accountability function of journalism.”
James T. Hamilton and Fred Turner, “Accountability through Algorithm: Developing the Field of Computational Journalism” (a report from Developing the Field of Computational Journalism, a Center for Advanced Study in the Behavioral Sciences Summer Workshop, July 27–31, 2009), 2.
Computational journalism, “Broadly defined ... can involve changing how stories are discovered, presented, aggregated, monetized, and archived.”
Sarah Cohen, James T. Hamilton, and Fred Turner, “Computational Journalism,” Communications of the ACM 54 (2011): 66
In analyzing the dimensions of computation that could advance sense making in journalism, Terry Flew and coauthors note: “Automation alleviates activities such as data gathering and interpretation, number crunching, network analysis, sorting, and processing that would otherwise need to be done manually; algorithms allow operators to follow predefined steps needed to accomplish certain goals, identify problems, find suitable solutions in a large set of alternatives, and verify information in a reliable, consistent and efficient manner; and abstraction enables the qualification of different levels or perspectives from which an idea may be presented or new directions that may be explored.”
Terry Flew, Christina Spurgeon, Anna Daniel, and Adam Swift , “The Promise of Computational Journalism,” Journalism Practice 6 (2012): 159
Though computational journalism builds on and incorporates elements of computer-assisted reporting and data journalism, this new approach often involves larger data sets and more sophisticated algorithms. Recent advances in computational journalism center on reporting by algorithms, about algorithms, and through algorithms. Stories generated by computer algorithms include those by Narrative Science and Automated Insights, companies seen as leaders in “Automation in the Newsroom.” The Wall Street Journal and ProPublica have each done reporting that looks at the disparate impacts of private and government algorithms on different groups in society. Reporters are also using algorithms to mine for stories, as the Atlanta Journal Constitution did in using web scraping and machine learning to identify potential cases across the country of doctors involved in sexual misconduct. The papers and video from the 2016 Computation + Journalism conference at Stanford show recent computational advances in story discovery, telling, and distribution.
Tuesdays, 1:30 - 3:20 p.m.
JSK Garage (Room 433), McClatchy Hall (Bldg. 120)
This course will explore the evolving field of computational journalism. Students will research and discuss the state of the field, and do projects in areas such as understanding the media ecosystem, stimulating media creation and assessing media impact.
Admission is by application.
Teaching Team
MANEESH AGRAWALA
Professor of Computer Science
My goal for the class is to work with the students and instructors to identify the open research problems and and areas that Computational Journalism (CJ) should focus on. Where can computational tools and techniques benefit journalism the most? How should journalists think about and incorporate computational methods into their work? What is the role of algorithms and computational methods in the newroom?
KRISHNA BHARAT
Founder, Google News
As a practitioner - and I’ve been active in the field of Computational Journalism since 1993 - I’ve seen a lot of evolution in computing’s ability to influence both the business and practice of journalism. Technology now intimately impacts how news is sourced, synthesized, disseminated and monetized. Further, we can use it as a tool to study the online news ecosystem as whole. In this course, our goal is to look at promising sub areas within Computational Journalism, to understand what’s been done, predict where technology is headed, and also identify future opportunities for research and innovation. My hope is to leverage the insights and vectors that emerge from this course as groundwork to inform investments and activities within the departments involved, including the funding of projects and fellowships.
DAWN GARCIA
Director, John S. Knight Journalism Fellowships at Stanford
Now, more than ever before, journalism is in an era of chaos and opportunity. My goal for this new course — Exploring Computational Journalism — is to advance the understanding of the challenges facing journalism today and the importance of journalism in a democracy. I’m eager for smart Stanford students from a range of disciplines — journalism, computer science, business, engineering and more — to explore those challenges together. The lessons and learning that comes out of this course will live on and inform the future of journalism, and in particular, the John S. Knight Journalism Fellowships here at Stanford. The JSK Fellowships is launching a new framework revolving around teamwork on areas articulated in this course. Come help us test out these ideas!
JAY HAMILTON
Hearst Professor of Communication
I believe that the evolution of computational journalism (CJ) can improve the economic prospects for public affairs journalism, particularly investigative reporting. On the supply side, CJ can lower the costs of discovering stories through better use of data and algorithms. On the demand side, CJ can make it possible to tell stories in more personalized and engaging ways. This can create product differentiation, so that a reader may seek out and be willing to pay for content form a particular outlet that is distinctive and highly valued. My hope is that Comm 281/CS 206 will produce work that helps identify specific ways that advances in computation can help journalists discover and tell stories in ways that are financially sustainable and that hold institutions accountable.
Interdisciplinary Collaboration
Sample Schedule
NOTE: The first version of COMM 281/CS 206 was taught in Winter 2017. The schedule for that class was:
Timeline:
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WEEK ONE (Jan. 10 and Jan. 12): Intro - How can you discover and tell stories through computational journalism?
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WEEK TWO (Jan 17. and Jan. 19): Team Formation and Problem Definition
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WEEK THREE (Jan. 24 and Jan. 26): AI, Data Science and Info Viz
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WEEK FOUR (Jan. 31 and Feb. 2): Emerging Hardware Tech: Drones, Sensors and VR
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WEEK FIVE (Feb. 7 and Feb. 9): Audience Participation and Diverse Viewpoints
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WEEK SIX (Feb. 14 and Feb. 16): Free Speech and Democracy
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WEEK SEVEN (Feb. 21 and Feb. 23): News Ecosystem and Business Models
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WEEK EIGHT (Feb. 28 and March 2): Project presentations, Tracks 1 and 2
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WEEK NINE (March 7 and March 9): Project presentations, Tracks 3 and 4
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WEEK TEN: Project presentation Track 5 (March 14) and final presentations (March 16)
The Fall 2017 version will cover similar topics, but will be more focused on projects related to solving challenges in these areas.
©2017 Stanford University.