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Text as Data (INAF U6514). Spring 2020, 2021, 2023, 2024

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, and machine learning classification of text. 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 (INAF U6506). Spring 2019, 2020, 2021, 2024.

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.

The Politics of Policymaking: Issues in Comparative Politics (PUAF U6130). Fall 2018, 2019, 2020, 2021, 2024.

Policymaking—the process by which political actors make decisions on a range of issues—is strongly influenced by context. The political environment in which policymakers interact plays a central role in shaping agendas, strategies, and choices. To be successful, policy professionals must be able to navigate a complicated set of political institutions that can constrain the menu of policy options, engage with multiple actors and stakeholders, and become familiar with dynamically changing technological and media environments. This course will give students important foundational knowledge on the way in which political contexts shape policymaking around the world.


The course has four parts. The first will focus on the policy process. We will learn what factors commonly influence policymakers’ decisions and discuss how solutions to policy problems can be evaluated in a policy analysis framework. The second part will focus on democracy and democratic erosion. We will learn about the rise and decline of democratic institutions and discuss factors that have shown to weaken democratic processes around the globe, including corruption, identity politics and polarization, and mis/disinformation.


In the third part of the course, we will delve into politics in the era of artificial intelligence. We will learn how AI tools such as large language models are shaping policy around the world, and discuss their potential impact on the information environment in a range of political domains. The final part of the course will focus on contentious politics. We will learn about recent debates on the politics of immigration, as well as protests and activism around the world, and discuss their influence on policymakers’ decision making.

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