The data science revolution is bringing unprecedented opportunities to social science research.  Never before have we had such enormity and variety of data, from endless social media streams to every survey imaginable, from outputs of every conceivable sensor and wireless device to massive consumer databases. We are seeing data in every form, every level of granularity and quality.  Social scientists’ newest challenge is to generate insight from the data for the political, economic and social wellbeing of individuals and our society.  The issues include developing tools and methods to process and understand the data, theoretical frameworks for the integration of new insight from such massive data to existing knowledge in social sciences, the applicability of new insight to appropriate contexts and new ethical considerations that are emerging in this age of Big Data.  The MIDAS Social Science Research Hub funds two initial research projects.  Through a variety of hub activities, MIDAS hopes to:

  • Disseminate tools and methods to empower campus-wide data-intensive social science research.
  • Build a collaborative network of social science researchers that will propel UM to the forefront of transportation research in the nation.
  • Form industry partnerships and transform research findings into social, political and economic insights that will inform businesses, policymakers and public services.

Computational Approaches for the Construction of Novel Macroeconomic Data

Nowcasting, the description of current events and events in the immediate future and immediate past, holds great promise for insight into social and economic phenomena based on tracking and analyzing online data.  Online data sources, such as social media text messages and images, capture a wide range of economic and social behaviors at high frequency and low cost, especially relative to traditional survey and administrative sources.  The research team will develop a data ingestion and archiving service that constantly records, processes, and archives text and image data from online sources, such as Twitter and government-sponsored traffic cameras.  They will also develop a nowcasting dataset construction tool for economists and other domain experts to transform the ingested and archived data streams into high-quality topic-specific nowcasts.

A Social Science Collaboration for Research on Communication and Learning based upon Big Data

One of the challenges facing social scientists is that our understanding of how social and political processes operate and what their consequences are has lost some of its predictive power, such as our failure to predict election outcomes.  This phenomenon raises questions of whether theories and models developed in the past – among a different generation living in a different cultural and technological setting – apply in the current environment.  Concurrently, the abundance of online, social media data provides the social scientists with great opportunities to understand today’s social and political phenomena.  To use such opportunities, however, important issues on how to process and use social media need to be addressed.