About the Event
James Thompson used simulation as a tool for learning. He advocated a “paradigm for realistic, evolutionary modeling” in which each iteration brings us closer to describing some state of the world. He had a belief in the power and adaptability of statistics, suggesting that perhaps the central notion of the profession is that “there is no methodological center, only an operational one, one basic mantra: Be ready to cope with ever-changing situations.”
For the last seven years, I have been a faculty member at Columbia Journalism School, where I have seen that the need to cope with change is the basic mantra of a good reporter as well. This expansiveness has led journalism to borrow methods from various disciplines, and in this talk I will focus on its relationship to the computing sciences.
Journalists have long turned to data and computation as important components in their reporting. In 1904, the founder of Columbia Journalism School, Joseph Pulitzer, advocated for the inclusion of data analysis in his College of Journalism. “You want statistics to tell you the truth” he explained, and then quickly pointed out that in statistics you can find “romance, human interest, humor and fascinating revelations.” Data and its analysis is a unique source for journalists, one with descriptive power on almost every beat.
In ways that would have been hard for Pulitzer to fully anticipate, data and computation now form complex systems of power in our world. And to fulfill journalism’s public mission, reporters need to be trained to both report on as well as with computational methods. As an example, I will present a project that began in my Computational Journalism class and considered how the mechanics behind the networks we rely on every day for information can be hacked.
In describing our project, I will return in a way to Thompson’s work — but with a plot twist. We uncovered vast networks of “bots” that iteratively inch closer to simulating authentic communication patterns, and offer their creators an outsized voice on platforms like Twitter and Facebook. My students’ work uncovering these simulation tactics was eventually published by the New York Times, and was part of a package of stories that was a finalist for a 2019 Pulitzer Prize.
Bio: Mark Hansen joined Columbia Journalism School in July 2012 and took on the position of inaugural director of the east coast branch of the Brown Institute for Media Innovation. Prior to joining Columbia, he was a professor at UCLA, holding appointments in the Department of Statistics, the Department of Design Media Arts and the Department of Electrical Engineering. He was also a Co-PI for Center for Embedded Networked Sensing, an NSF Science and Technology Center devoted to the study of sensor networks. Prior to UCLA, Hansen was a Member of the Technical Staff at Bell Laboratories in Murray Hill, New Jersey.
For nearly three decades, Hansen has been working at the intersection of data, art and technology. Hansen has an active art practice involving the presentation of data for the public. His work with Ben Rubin, Jer Thorp and The Office for Creative Research has been exhibited at the Museum of Modern Art in New York, the Whitney Museum, the Centro de Arte Reina Sofia, the London Science Museum, the Cartier Foundation in Paris, and the lobbies of the New York Times building and the Public Theater (permanent displays) in Manhattan.
In terms of his journalistic experience, Hansen has been a long-standing visiting researcher at the New York Times R&D Lab, a late-career intern at the Marshall Project, and a consultant with HBO Sports. Hansen teaches mainly advanced data analysis and computational journalism at Columbia. In 2018, Hansen’s Computational Journalism course at Columbia Journalism School contributed the original reporting for the New York Times’ piece, The Follower Factory (link is external), which exposed the bot economy behind the sale of fake followers on Twitter. In July 2018, the article was cited by Twitter as the reason for its “purge” of tens of millions of suspicious accounts.
Hansen holds a B.S. in Applied Math from the University of California, Davis, and a Ph.D and M.A. in Statistics from the University of California, Berkeley. He has been awarded eight patents and has published over 60 papers in data science, statistics and computer science.
