Teaching
Text as Data
This course is an introduction to the quantitative analysis of text as data – a rapidly growing field within the social sciences. The availability of textual data has grown massively in recent years, and so has the demand for skills to analyze it. Vast amounts of digital content are becoming increasingly relevant to various policy-relevant questions. For example, social media data are now commonly used to understand public opinion, engagement with politics, behavior during natural disasters, and even pathways to extremism; candidates' statements and rhetoric during elections are useful for estimating policy positions; and large amounts of text from news sources are used to document and understand world events.
While the wealth of information in text data is incredible, its sheer size makes it challenging to summarize and interpret without quantitative methods. In this course, we will learn how to quantitatively analyze text from a social-science perspective. Throughout the course, students will learn different methods to acquire text, how to transform it to data, and how to analyze it to shed light on important research questions. Each week we will cover different methods, including dictionary construction and application, sentiment analysis, scaling and topic models, machine learning classification of text, and emerging applications of Large Language Models. Lectures will be accompanied by hands-on exercises that will give students practical experience while working with real-world texts. By the end of the course, students will develop and write their own research projects using text as data.
Data Science and Public Policy
In our digital age, data are everywhere. According to recent estimates, over 90% of current global data were generated in the last two years. With internet usage reaching almost two thirds of the world’s population, this trend is likely to increase. The vast amount of information generated by humans, machines, and even nature is becoming increasingly relevant in various policy areas. Social media data are now commonly used to understand – and influence – a broad range of political phenomena; machine learning algorithms increasingly influence decision-making, high-frequency data allow observing dynamic social and political processes that were harder to detect in the past; and Generative AI is believed to transform many areas of social and public life in the future.
As a result, there is a growing need for policy professionals to understand data science, and for data scientists to become familiar with important policy issues. Combining policy expertise with data science skills has the potential to produce powerful positive societal outcomes. Yet, there are few opportunities for policy and data science students to work together.
This course will bridge the gap between data science and public policy in several exciting ways. By drawing on a diverse student body – consisting of students from SIPA, the Data Science Institute, the Quantitative Methods in the Social Sciences, the Statistics and Computer Science programs – we will combine domain-level policy expertise with quantitative analytical skills as we work on cutting-edge policy problems with large amounts of data.
Throughout the semester, students will have the opportunity to analyze and examine real-world datasets on a broad range of policy topics, including, for example, data on disinformation campaigns on social media, data relating to privacy and computer vision, and granular information on natural disasters that can facilitate preparedness for future hazards. In addition, students will work in interdisciplinary policy – data science teams on semester-long projects that develop solutions to policy problems drawing on big data sources. By the end of the course, students will gain hands-on experience working with various types of data in an interdisciplinary environment – a setting that is becoming more and more common in the policy world these days.
Democracy and Democratic Erosion in the AI Era
As technology shapes how we live and communicate, democracies face a host of old and new challenges. This 7-week course explores the drivers of democratic erosion, such as polarization, corruption, and the influence of money in politics, and considers how digital technologies, including artificial intelligence, are accelerating these threats. We’ll examine both the structural vulnerabilities of democratic institutions and the evolving tools used to exploit them.
We begin with foundational theories of democracy, focusing on how different systems function and what makes them fragile. From there, we analyze the processes and warning signs of democratic backsliding. In later weeks, we turn to the role of AI and algorithmic systems in either undermining or reinforcing democratic resilience. Through a mix of classic texts and contemporary research, students will gain a deeper understanding of how democracies erode—and the political, institutional, and technological strategies that can help defend them in the digital age.