Data, Media & Society

The Center for Data, Media & Society (formerly Center for 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 & 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 & 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!

Featured Research

"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.
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.


Peter Bearman:

After Tobacco: What would Happen if Americans Stopped Smoking? (2011)

Dan Ellis:

Speech Decoloration Based On The Product-Of-Filters Model (2014)

May, Chaintreau, Korula, Lattanzi:

Filter & Follow: How Social Media Foster Content Curation (2014)

Gary Natriello:

Adaptive Educational Technologies and Educational Research: Opportunities, Analyses, and Infrastructure Needs (2012)

Prabhakaran and Owen Rambow:

Written Dialog and Social Power: Manifestations of Different Types of Power in Dialog Behavior (2013)
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