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. Yet macroeconomic measurements derived from social media data have not yet become mainstream, for three main reasons. First, the software systems and deployment are too difficult for most users. Second, new data sources such as image streams require novel computational approaches before they can be useful to domain experts. Third, projects to date have been either entirely manual (and thus too burdensome for most) or entirely automatic (and thus unable to exploit economists’ expertise). This project aims to build a software system that helps overcome these burdens.
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. For example, an economist might use the tool to build an unemployment predictor, while a stock analyst might use it to predict the opening weekend box office for an important studio’s releases. It entails research efforts in computer vision, machine learning, and data management systems. The team will also build an economics datapedia that collects and publishes a range of nowcast-driven datasets built using the dataset construction tool, similar to YouTube or other social platforms. This system allows economists and other domain experts to discuss, combine, and criticize datasets.
Together, these components should make social media a powerful tool for the construction of economic measures by practicing economists. This project will produce novel research, and by also building tools, services, and datasets, will make that research substantially more impactful, long-lasting, and useful to other researchers.
Matthew Shapiro, Principal Investigator, Professor, Department of Economics and Survey Research Center
Michael Cafarella, Associate Professor, Department of Electrical Engineering and Computer Science
Jia Deng, Assistant Professor, Department of Electrical Engineering and Computer Science
Margaret Levenstein, Research Professor, Institute for Social Research
The team will develop tools for classifying goods and services including approaches that mix images and text (an expertise of the computer science members of the project). Are two goods similar because they look the same, or because they are highly substitutable in consumer demand? This classification problem is a leading example of how to combine computer science and economics approaches.