Big Data for the Social Sciences

Year: 2013  
Cluster Leads:

Arnout van de Rijt, Ph.D.
Associate Professor
Department of Sociology and IACS

SBS Building
Stony Brook University
Stony Brook, NY 11794-4356

Phone: 631-632-7704


Steven Skiena, Ph.D.
Distinguished Teaching Professor
Department of Computer Science

Computer Science Building
Stony Brook University
Stony Brook, NY 11794-4400

Phone: 631-632-9026


Academic Units and Collaborating Faculty: Sociology Arnout van de Rijt
Michael Schwartz
Jen Heerwig
Computer Science Steven Skiena
Jie Gao
Arie Kaufman
Political Science Matthew Lebo
Jeffrey Segal
Institute for Advanced Computational Science (IACS) Robert Harrison
Arnout van de Rijt


The last ten years have yielded an explosion in the availability of data extracted from traces of human behavior, including cell phone records with GPS tracking, search engine queries, internet transaction data, consumer behavior, or social network activity. These very large datasets and the patterns they reveal form a honey pot for social scientists, enabling them to test new hypotheses and study phenomena on a previously unprecedented scale. The technological challenges have intrigued scholars from such distant disciplines as physics and computer science, sparking a new Big Data revolution in the social sciences. Big Data cluster will exploit the new opportunities to harvest and analyze vast amounts of information from a wide variety of digital sources to answer tantalizing questions at the research frontier. Stony Brook currently houses some of the world leading social and computer scientists who have made major scientific advances taking advantage of these Big Data opportunities. The cluster leads have already established fruitful collaborations between computer science and their respective social science disciplines: using computational sentiment analysis methods to explore public support for the Iraq War, studying the cumulative advantage of fame through large-scale newspaper analysis, and the mystery of how we effectively find the six degrees of separation in social networks. This Big Data initiative will expand the activities to create a critical mass of scholarship in this mushrooming area.

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