Data Blending & Integration

More and more unstructured, organic data related to human behavior, beliefs, and opinions are being shared online. Because of their availability and richness, these data are an important source of information for social scientists attempting to characterize and predict human and societal dynamics. They give insights that traditional survey data can miss and can be less costly to collect. The researchers working on data blending and integration consider issues related to combining data across multiple data sources, some of which may be organic, administrative, qualitative, and/or survey data. We focus on methods for constructing and combining variables from numeric, categorical, open-ended text, and image data. 

S3MC: This project focuses on developing methods for using social media data to better understand public opinion as it relates to politics, the economy, and parenting. It also considers the role misinformation plays in public perceptions.

Forced Migration: This project develops methods and tools for using big data in conjunction with traditional administrative and survey data to better understand and predict where and when people will move when they are forcibly displaced.


Shweta Bansal (She/Her)

Department of Biology, Provost’s Distinguished Associate Professor

Robin Dillon-Merrill (She/Her)

McDonough School of Business Operations and Analytics, Professor and Area Head

Dayanand Manoli (He/Him)

McCourt School of Public Policy, Associate Professor

Amy O’Hara (She/Her)

Director of Georgetown Federal Statistical Research Data Center
MDI, Research Professor

Lisa Singh (She/Her)

Director, Massive Data Institute
Sonneborn Chair | Chair and Professor, Department of Computer Science | Professor, McCourt School of Public Policy