Announcing New Computational Social Science Grants for Early Career Scholars

May 12, 2020

RSF is pleased to announce the winners of the second round of the computational social science grants competition for early career scholars. The digital age has rapidly increased access to large and comprehensive data sources and unique new sources of information from online transactions, social media interactions, and internet searches. These grants fund research projects that bring new data and analytical methods to bear on questions of interest in the foundation’s core program areas.

The following five projects were funded during this round of grant making.

Kevin Munger (Pennsylvania State University) and James Bisbee (Princeton University) will examine the salience of popular political beliefs online as well as the inequality in Twitter attention between higher- and lower-ranked institutions as well as early career and more advanced scholars.

Yu Ding (Columbia University) will explore the use of a method to leverage the input of the general population (crowdsourcing), algorithms (supervised learning), and experts (third-party checkers) to detect false information in news media.

May ElSherif and Diyi Yang (Georgia Tech) will work to define implicit discriminatory speech and design generic and scalable data-driven techniques to detect this speech on social media.

Nynke Niezink (Carnegie Mellon) will develop efficient methodology for analyzing wiki survey data and create an interactive web app that communicates wiki survey results.

Pierre-Luc Vautrey, Charlie Rafkin, and Advik Shreekumar (MIT) will study how fear and anxiety affect economic preferences, behavioral biases, and political preferences using online surveys and experiments administered during the COVID-19 pandemic.

 

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