Networks

This graduate course will train you in the methods, conceptual approaches, and theories of computational social science. It is set up to welcome people from many different backgrounds, particularly those with or without prior exposure to programming, statistics, or the social sciences. You will learn how to design research projects and answer research questions motivated and situated in social sciences discourses and theories. The course will survey canonical and cutting-edge methods and techniques. This includes methodological approaches in Big Data analysis, data visualization, social network analysis, agent-based modeling, and natural language processing. You will learn to identify and develop variables, mechanisms, and theoretical framing grounded and motivated within the social sciences. We will also address the growing ethical challenges and considerations associated with computational social science methods and approaches.

When people use the internet, they leave behind traces of their political behaviors and social interactions across space and time. While these digital traces are typically created and collected by businesses and governments for their own internal purposes, they are often available to researchers either incidentally or explicitly on behalf of these organizations. Digital trace data have the potential to broaden the scope and scale of political and social research, but require knowledge of computational tools and methods that are typically not taught to social scientists. Moreover, digital trace data present new legal and ethical considerations, since they often contain sensitive, individual-level information. Digital trace data also present conceptual challenges, some which are not new to political and social scientists, like representativeness, and others that are unique to the web, including algorithmic confounding.

This course introduces theories and methods used for the analysis of political networks. Political networks describe how political actors - such as participants of the policy process - form and maintain relationships, and the analysis of political networks can help us to understand how these relationships influence political or policy outcomes. Network concepts are increasingly prevalent across a wide range of social science disciplines, and are often used as a tool to study complex phenomenon such as cooperation, diffusion of innovation, and social capital. This course will introduce students to major research questions in the study of networks, as well as their applicability to understand real-world problems in public policy and political science. Students will learn, through hands-on training in R, how to manage network data and perform essential descriptive and inferential analyses on these data.

Contributions of the ethnography of communication, language variation studies, and conversation/discourse analysis to the interdisciplinary development of sociolinguistics.

Although standard graduate statistics courses prepare students to design and run statistical analyses, courses generally do not spend a great deal of time discussing data management and workflows, which are critical to making research replicable, efficient, and accurate. This is unfortunate because the best designed statistical analysis is easily undone by poor data management, whether through misconstructed variables, unreplicable workflows, and/or poorly commented or documented workflows and programs. This course addresses this oversight by focusing on computational tools for data management and workflows, including using software packages such as Stata and python.

The logic and methods of social network analysis. Emphasis on theoretical underpinnings and applications to sociological research.

Qualitative and quantitative methods for research on social networks (the patterning of relations among actors ranging from persons to nations), emphasizing a "how-to" approach to analyzing existing datasets or those collected by seminar participants in their own research.

The methods and tools of Artificial Intelligence used to provide systems with the ability to autonomously problem solve and reason with uncertain information. Topics include: problem solving (search spaces, uninformed and informed search, games, constraint satisfaction), principles of knowledge representation and reasoning (propositional and first-order logic, logical inference, planning), and representing and reasoning with uncertainty (Bayesian networks, probabilistic inference, decision theory). Graduate-level requirements include additional reading of supplementary material, more rigorous tests and homework assignments, and a more sophisticated course project. Sophisticated application and technique.

Quantitative methods in political science and policy research are changing rapidly. The rise of the internet has brought in new sources of text, network, geographical, image, video, and other data. Meanwhile, computing storage and processing capabilities continue to expand, while data and code sharing norms have made it so that anyone with a computer and internet connection can have access to a growing set of tools and methods for modeling and interpreting patterns. This course focuses on the extraordinary work that is emerging in politics and policy as a result of these recent advances, with a broad set of applications ranging from health and defense to environmental and agricultural policy. The course highlights current trends, challenges, and new directions for political and policy researchers in academia, government, and the private sector, focusing on how these new data sources and methodologies are being used to solve problems in social science and public policy.

This course will guide students through advanced applications of computational methods for social science research. Students will be encouraged to consider social problems from across sectors, like health science, education, environmental policy and business. Particular attention will be given to the collection and use of data to study social networks, online communities, electronic commerce and digital marketing. Students will consider the many research designs used in contemporary social research and will learn to think critically about claims of causality, mechanisms, and generalization in big data studies. Graduate requirements include additional readings and a more in-depth final paper than is required at the undergraduate level.