CAREER: Promoting Equal Opportunities through Measurement, Simulation, and Education

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This research will advance our ability to draw on large-scale data from online social media to understand attitudes towards racial inequality in the United States and their correlations with critical dimensions of social structure. While a large literature has studied racial bias on social media, there has been little research on what social media users believe about the existence and causes of racial inequality. While much is understood about these beliefs in the social sciences, this project will develop new methods to measure these beliefs at an unprecedented scale, allowing a new understanding of the distribution of racial ideologies and their correlates with place, identity, and network structure. The long-term research goal is to expand knowledge on how computational methods can be constructively used alongside social theory and large datasets to help address social inequality. This work will include the development and release of tools that will allow qualitative scholars to leverage similar data without expertise in machine learning. In addition, this proposal aims to advance computer science education via the development of new curriculum and opportunities for undergraduate research. This project has four fundamental research objectives. First, it will develop novel mixed methods approaches that combine modern graph-based and natural language machine learning methods with qualitative analysis to identify dominant beliefs about racial inequality expressed in large datasets from Twitter and Parler. Second, it will collect and analyze an innovative new dataset linking social media and survey data on attitudes about racial inequality, providing insight into discrepancies between these two sources of measurement. Third, it will combine measurements of social media users' views on racial inequality with data on where they live and the structure of their social networks to study how place, identity, and network structure are correlated with racial ideology. Fourth, it will develop a novel agent-based simulation framework that combines empirical findings with contemporary theory on racial inequality, that can be used to evaluate the strengths and weaknesses of different proposed interventions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.