Computational Social Science

Overview

<div>Social science research on many topics has often been hampered by the limitations of survey data. However, the digital age has rapidly increased access to large and comprehensive data sources such as public and private administrative databases, and unique new sources of information from online transactions, social-media interactions, and internet searches. New computational tools also allow for the extraction, coding, and analysis of large volumes of text. Advances in analytical methods for exploiting and analyzing data have accompanied the rise of these data. The Russell Sage Foundation’s special initiative on Computational Social Science (CSS) supports innovative social science research that brings these new data and methods to bear on questions of interest in its core programs in Behavioral Economics, Future of Work, Race, Ethnicity and Immigration, and Social Inequality.</div> <div></div> <div>Social science research on many topics is often hampered by the limitations of survey data, including relatively small sample sizes, low response rates and high costs. However, the digital age has increased access to large, comprehensive data sources, such as public and private administrative databases, and new sources of information from online transactions, social-media interactions, and internet searches. New computational methods also allow for the extraction, coding, and analysis of large volumes of text. Advances in analytical methods for exploiting and analyzing data, including machine learning, have accompanied the rise of these data. The emergence of these new data and methods also raises questions about access, privacy and confidentiality.</div> <div></div> <div>The Russell Sage Foundation’s initiative on Computational Social Science (CSS) supports innovative social science research that brings new data and methods to bear on questions of interest in its core programs in Behavioral Economics, Future of Work, Race, Ethnicity and Immigration, and Social Inequality. Limited consideration will be given to research that focuses primarily on methodologies, such as causal inference and innovations in data collection.</div> <div></div> <div>Examples of research (some recently funded by RSF) that are of interest include, but are not restricted to, the following:</div> <ul> <li>Small Grants Competitions with Big Da (small grants were due 3/15/2019)</li> <li>Linked Administrative Data</li> <li>Algorithms and Automated Decision-Making</li> <li>Private Administrative Data</li> <li>Machine-Learning</li> <li>Online Surveys and Experiments</li> <li>Text Analysis</li> <li>Social Media</li> </ul>