Computational Social Science

author: Hanna M. Wallach, Department of Computer Science, University of Massachusetts Amherst
published: Dec. 5, 2015,   recorded: October 2015,   views: 3262

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 1:15:45
Watch Part 2
Part 2 45:35


From interactions between friends, colleagues, or political leaders to the activities of corporate or government organizations, complex social processes underlie almost all human endeavor. The emerging field of computational social science is concerned with the development of new mathematical models and computational tools for understanding and reasoning about such processes from noisy, missing, or uncertain information. Computational social science is an inherently interdisciplinary area, situated at the intersection of computer science, statistics, and the social sciences, with researchers from traditionally disparate backgrounds working together to answer questions arising in sociology, political science, economics, public policy, journalism, and beyond. In the first half of this tutorial, I will provide an overview of computational social science, emphasizing recent research that moves beyond the study of small-scale, static snapshots of networks, and onto nuanced, data-driven analyses of the structure, content, and dynamics of large-scale social processes. I will focus on commonalities of these social processes, as well as differences between the types of modeling tasks typically prioritized by computer scientists and social scientists. I will then discuss Bayesian latent variable modeling as a methodological framework for understanding and reasoning about complex social processes, and provide a brief overview of Bayesian inference. In the second half of this tutorial, I will concentrate specifically on political science. I will discuss data sources, acquisition methods, and research questions, as well as the mathematical details of several models recently developed by the political methodology community. These models, which draw upon research in machine learning and natural language processing, not only serve as examples of the outstanding methodological work being done in the social sciences, but also demonstrate how ideas originally developed by computer scientists can be adapted and used to answer substantive questions that further our understanding of society.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Reviews and comments:

Comment1 aberash chala, February 12, 2016 at 10 p.m.:

very good

Write your own review or comment:

make sure you have javascript enabled or clear this field: