The Center for Data, Media and Society (formerly New Media) is interested in the human in data. It is comprised of students and faculty who are engaged in both creative as well as research practices grounded in data. We study the ways in which we can use data to understand human behavior, and we address questions about how data and data processing are shaping how we work, how we live, and what it means to be person in a networked, digitized world.
In the Center for Data, Media and Society, we use data generated by people and data about people -- from the Tweets and status updates of social media, to images and video culled online, to large quantities of text. We design and build new tools for using data collections inside and outside of the University.
Projects include uncovering the pattern of official secrecy by examining databases of declassified documents, a “personalized” news engine that creates a kind of algorithmic editorial voice, and a visual study of Thomson-Reuter’s Web of Science. Columbia has a long track record of startups in the new media field, including Newsblaster, MPEG, Dygest, and Musically Intelligent Machines.
The Center for Data, Media and Society draws on participants from the fields of Architecture, the Humanities, the Social Sciences, Education, Journalism as well as Computer Science and Engineering. We are a diverse group of creative technologists, designers and scientists. Join us!
CALL FOR STUDENT RESEARCHERS, FALL 2018
The DSI Center for Data, Media & Society is pleased to announce our fall 2018 call for student research assistants (RAs). The purpose of the program is to identify promising undergraduate, masters, or graduate students to accelerate collaborative, high-impact research projects in the humanities (broadly defined).
Students should submit an up-to-date CV and a brief (~250 word) statement of interest one or more of the project(s) listed below. If there is interest, you will be contacted by the researchers or DSI affiliates to schedule an interview. Please note that these are *not* federal work-study positions, and accepting an RA appointment from DSI may limit your ability to take on other projects during the semester in question. Although the final hours will be determined by the faculty project leads, a typical commitment for any of the projects is approximately 10 hours/week.
Student applications should include:
Name, UNI, program, year, and major/focus (if applicable)
The name(s) and statement(s) of interest for the projects the student would like to be considered for
An up-to-date CV
- The names and contact information of up to two (2) individuals who can offer a reference (optional)
Sunday, September 23, 2018 11:59PM EST: Applications due
Monday, September 24 - Friday, September 28, 2018: Interview period for select applicants
- October 1, 2018: Final candidates selected and notified
Submission link (Dropbox): https://www.dropbox.com/request/tjvlvQAjhycBZqsHmoSc
FALL 2018 PROJECTS
Title: Hacking Voter Suppression
Faculty: June Cross (Journalism/Documentary Film); Mark Hansen (Journalism/Statistics)
Project Description: This project will explore how Russian interference combined with gerrymandering and domestic legal challenges, including voter ID laws, to suppress the black vote in 2016. We will use big data to inform “shoe leather” reporting, and present projected data, pre-recorded audio interviews, and some re-enacted interviews in a theatrical setting. We will include historical video archives and develop a production design for five 3-5 minute videos.
The project will use actors, video designers, and a dynamic graphics interface to demonstrate the suppressive impact on the African American vote. Each “chapter” will combine scripted and improvised elements based on reporting and hard data analysis derived from the 2016 election.
Skills sought: We are seeking a self-starter data student who can both examine and initiate questions pertaining to social media data. Through a collaboration with researchers at Clemson University, we have access to the Twitter profiles, tweets and lists of followers for the most active accounts in the last week before the 2016 election.
Beyond social media data, DSI student collaborators will have a degree of autonomy to include
demographic data from the Census, spatial data describing voting districts, local surveys and other information to help describe the conditions leading up to the vote in 2016. The ultimate goal is a nuanced interplay between data, documentary interviews and theatrical performance. Given the timing, we will also try to capture social media activity during the 2018 midterm elections, relying on DSI students to collect data from Twitter similar to that provided by Clemson in 2016.
Our student collaborator should be fluent in either Python or R, and should have experience with text and network analyses, and, preferably, social media data. An ideal candidate should also have an independent spirit, able to ask questions of a data set as they pursue our project goals.
Eugene Wu, Computer Science (Chair)
Susan McGregor, Journalism (Co-Chair)
Mark Hansen, Journalism
Manan Ahmed, History
Shih-Fu Chang, Electrical Engineering and Computer Science
Kathy McKeown, Computer Science
Peter Allen, Computer Science
Asim Ansari, Business/Marketing
Emily Bell, Journalism
Omar Besbes, Business/Decision, Risk, and Operations
Augustin Chaintreau, Computer Science
Michael Collins, Computer Science
Matthew Connelly, History
Steven Feiner, Computer Science
Luis Gravano, Computer Science
Eitan Grinspun, Computer Science
Julia Hirschberg, Computer Science
John R. Kender, Computer Science
Bruce M. Kogut, Business
Laura Kurgan, Graduate School of Architecture, Planning and Preservation
Tamar Mitts, School of International and Public Affairs
Smaranda Muresan, Center for Computational Learning Systems
Suresh Naidu, Economics
Gary Natriello, Sociology
Shree K. Nayar, Computer Science
David K. Park, Institute for Social and Economic Research and Policy
Desmond Upton Patton, Social Work
Dan Rubenstein, Computer Science
Henning Schulzrinne, Computer Science
Dennis Tenen, English and Comparative Literature
Duy Linh Tu, Journalism
Barbara Tversky, Teachers College
John Wright, Electrical Engineering
CURRENT AND PREVIOUS PROJECTS
This project aims at using NLP to analyze large amounts of textual and speech data (an in particular interactive data) to find relations among people, and between people and propositions (such as sentiment or belief), and to identify when such relations change in an unexpected manner.
The enormous growth in the number of official documents - many of them withheld from scholars and journalists even decades later - has raised serious concerns about whether traditional research methods are adequate for ensuring government accountability. But the millions of documents that have been released, often in digital form, also create opportunities to use Natural Language Processing (NLP) and statistical/machine learning to explore the historical record in very new ways.
"The Listening Machine - Sound Source Organization for Multimedia Understanding" is an NSF-funded project at LabROSA concerned with separating and recognizing acoustic sources in complex, real-world mixtures.
Center for Spatial Research
Columbia's Groups for Experimental Methods in the Humanities
EdLab - http://edlab.tc.columbia.edu
Interdisciplinary Center for Innovative Theories and Empirics (INCITE)
Lab for Recognition and Organization of Speech and Audio (LabROSA)
Written Interaction and Social Relations (WISR)