Social science takes as axiomatic that agents act on beliefs: however, teasing out the correlations between belief and action is no simple feat. Traditionally, social scientists relied on survey data to measure sentiment that might indicate changing economic trends, and on lab experiments to test actions given manipulated beliefs. Surveys, however, are notoriously expensive to scale, difficult to conduct frequently, and possess bias; lab experiments artificially constrain decision-making and thus fail to capture the complex dependencies of real-world actions.

This project will generate measurements of public sentiment directly from the actions of people, using one of the largest datasets of human behavior ever studied: online opinions expressed on platforms like Twitter or comments posted on news websites, time series representing the volume of search queries on Google, and call detail records. Using these digital footprints, the project will develop new social-computational measures of public sentiments related to the state of the economy, expected unemployment, and concerns about national priorities.

Broader impacts: The project will offer new alternatives to surveys as a measure of public sentiment, and will also generate unprecedented insight into the online and onsite behavior of the American population. This research also has the potential to help public and private organizations better understand the dynamic behaviors of customers and constituents, and to make business and policy decisions informed by economic trends derived from data. The project will enhance education through the interdisciplinary training of graduate students. Both graduate and undergraduate students from underrepresented groups will be actively encouraged to participate in the project. A public website will also be set up detailing the project results.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
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Tatiana D. Korelsky
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Santa Fe Institute
Santa Fe
United States
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