The increasing use of the Internet and online communities in the last decade has led to an explosion of social and behavioral data capturing every aspect of our daily activities – from what we like, what we read, to where we go, whom we know and beyond.  This in-turn has led to the rise of computational social science – an emerging field which provides the opportunity to empirically study human behavior by applying computational methods, algorithms, and models on “big social data”.

In this course, students will adopt an interdisciplinary approach to empirically study different social phenomena with applications to social science fields, such as political science, sociolinguistics and sociology. The empirical approach will span a variety of quantitative methods, including applying existing machine learning tools and natural language processing techniques. In this seminar-style class, students will investigate several academic readings from this emerging field. They will also work on a semester-long research project in groups of 2-3 students. The goal of the project is to identify a question or problem that can be addressed by analyzing social data, with the broader goal of addressing larger societal issues such as ideological segregation, deviant behavior, online misinformation etc.