Lessons from a computational political scientist
**Sarah Cohen, The New York Times (moderator)
When reporters take on document analysis, we sometimes forget that content analysis has been a mainstay of political and other social sciences for generations. We'll learn about both traditional and cutting edge techniques from Amber Boydstun, a political scientist at the University of California at Davis whose projects include finding jokes in Federal Reserve meeting transcripts and media agenda-setting analysis.
Amber E. Boydstun (Ph.D.) is associate professor of political science at UC Davis. She uses lab experiments, large-scale media studies, and manual and computational text analysis to study how issues make the news, the dynamics of “media storms,” and how media attention shapes public opinion. She is author of Making the News (Chicago) and co-author of The Decline of the Death Penalty and the Discovery of Innocence (Cambridge), as well as many journal articles.
Sarah Cohen is the Knight Chair in Journalism at the Walter Cronkite School at ASU. Previously, she worked as the editor of a data reporting team at The New York Times focused on long-term enterprise and investigative stories, and as a database editor for The Washington Post. Her awards include the Pulitzer Prize in Investigative Reporting, the Goldsmith Prize and the IRE medal. She is a past president of IRE, and served on the board for eight years.
Lessons from a Computational Political Scientist
These slides walk us through how to political computational science can be applied to reporting. You can learn about how to use texts to find patterns and tools political scientists use to analyze data.