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 PROPOSALS, SPRING 2019
The DSI Center for Data, Media & Society is pleased to announce our call for spring project proposals. 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).
Selected projects must ensure that at least 2 research mentors - one in humanities and one in DSI/CS - will meet weekly with each funded student. Larger groups of researchers can co-mentor student(s) in rotation throughout the semester, as long as at least two researchers (one from each discipline) are present at each weekly meeting.
Supported projects must submit a 3-5-page progress report in mid-May, outlining the following:
- A description of the specific work pursued over the course of the semester, and how it moved forward the overall research project/agenda
- Links to any outputs (e.g. papers, articles or other media, code, published interviews &c.)
- 2-3 paragraphs from each faculty mentor and student involved reflecting on the impact of the work on their own broader academic or research goals and development
- Next steps for the project
Project Selection and Support
Up to 4 accepted projects will be awarded funding for one student with a stipend of up to $2,750. For interested faculty who have not identified a particular student they would like to support, the Center for Data, Media & Society will be soliciting applications from interested students who may be a good fit for your project. Project mentors will then be asked to select a student from those provided. A committee of center members will review the proposals and allocate these funds.
Project proposals should be a maximum of 1 page, and include the following information:
- Name, title, and email of humanities faculty mentor(s)
- Name, title, and email of DSI/CS research mentor(s)
- Project title and description. This brief overview should emphasize the impact of the project and how participating in this program can uniquely accelerate the research.
- If the project has previously received support from DSI or the Center (for example, the 2018 DSI Scholars summer program), the application should include a paragraph describing what was achieved during the summer term and how the work can be importantly extended by continued support. Returning projects are encouraged to apply, but ongoing funding is not guaranteed.
- January 21, 2019, 11:59PM: Proposals due
- January 25, 2019: Selected proposals announced
- May 17, 2019: 5-page progress report for supported projects due
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.
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)