From the evening of Sunday, June 18 to the morning of Saturday, July 1, 2017, the Russell Sage Foundation will sponsor the first Summer Institute in Computational Social Science, to be held at Princeton University. The purpose of the Summer Institute is to introduce graduate students, postdoctoral researchers, and beginning faculty to computational social science. The Summer Institute is for both social scientists (broadly conceived) and data scientists (broadly conceived). The co-organizers and principal faculty of the Summer Institute are Christopher Bail and Matthew Salganik.
The instructional program will involve lectures, group problem sets, and student-led research projects. There will also be outside speakers who conduct computational social science research in academia, industry, and government. Topics covered include text as data, website scraping, digital field experiments, non-probability sampling, mass collaboration, and ethics. There will be ample opportunities for students to discuss their ideas and research with the organizers, other participants, and visiting speakers. Because we are committed to open and reproducible research, all materials created by faculty and students for the Summer Institute will be released open source.
Participation is restricted to Ph.D. students, postdoctoral researchers, and untenured faculty within 7 years of their Ph.D. Most participant costs during the workshop, including housing and most meals, will be covered, and most travel expenses will be reimbursed up to a set cap. About thirty participants will be invited. Participants with less experience with social science research will be expected to complete additional readings in advance of the Institute, and participants with less experience coding will be expected to complete a set of online learning modules on the R programming language. Students doing this preparatory work will be supported by a teaching assistant who will hold online office hours during the two months before the Institute.
To apply, please visit the link above.
Matthew Salganik is Professor of Sociology at Princeton University, and he is affiliated with several of Princeton’s interdisciplinary research centers: the Office for Population Research, the Center for Information Technology Policy, the Center for Health and Wellbeing, and the Center for Statistics and Machine Learning. His research interests include social networks and computational social science. He is the author of the forthcoming book Bit by Bit: Social Research in the Digital Age.
Chris Bail is the Douglas and Ellen Lowey Associate Professor of Sociology and Public Policy at Duke University and a member of the Interdisciplinary Program on Data Science, the Duke Network Analysis Center, and the Duke Population Research Institute. His research examines how non-profit organiations and other political actors shape social media discourse using large text-based datasets and apps for social science research. He is the author of Terrified: How Anti-Muslim Fringe Organizations Became Mainstream.
Sandra González-Bailón is an Assistant Professor at the Annenberg School for Communication, and affiliated faculty at the Warren Center for Network and Data Sciences. Prior to joining Penn, she was a Research Fellow at the Oxford Internet Institute, where she is now a Research Associate. Her research lies at the intersection of network science, data mining, computational tools, and political communication. She leads the research group DiMeNet (Digital Media, Networks, and Political Communication).
Deborah Estrin is Associate Dean and Professor of Computer Science at Cornell Tech in New York City and a Professor of Public Health at Weill Cornell Medical College. She is founder of the Health Tech Hub and directs the Small Data Lab at Cornell Tech, which develops new personal data APIs and applications for individuals to harvest the small data traces they generate daily. Estrin is also co-founder of the non-profit startup, Open mHealth.
Gary King is the Albert J. Weatherhead III University Professor at Harvard University, based in the Department of Government (in the Faculty of Arts and Sciences). He also serves as Director of the Institute for Quantitative Social Science. King and his research group develop and apply empirical methods in many areas of social science research, focusing on innovations that span the range from statistical theory to practical application.
Michael Macy is the Goldwin Smith Professor of Arts and Sciences in Sociology and Director of the Social Dynamics Laboratory at Cornell. With support from the National Science Foundation, the Department of Defense, and Google, his research team has used computational models, online laboratory experiments, and digital traces of device-mediated interaction to explore a variety of familiar but enigmatic social patterns such as critical mass and mobilization, network-based contagion, and political polarization.
Winter Mason is a Data Scientist at Facebook. He studies social networks, social media, crowdsourcing, and group dynamics. His research combines traditional psychological methods such as lab experiments with new methods such as online data collection with crowdsourcing and machine learning. His research has appeared in the Proceedings of the National Academy of Sciences and the Journal of Personality and Social Psychology, among other leading journals. He received his PhD in Social Psychology and Cognitive Science from Indiana University in 2007.
Markus Mobius is a Principal Researcher at Microsoft Research who studies the economics of social networks. He builds models of learning, coordination, and cooperation within social networks, with a particular focus on trust. His research employs lab and field experiments to study social networks in real settings. His work has been funded by the National Science Foundation and the Sloan Foundation, and published in leading journals such as the American Economic Review and the Quarterly Journal of Economics. He completed his PhD in economics from the Massachusetts Institute of Technology in 2000.
Brandon Stewart is Assistant Professor of Sociology at Princeton University where he is also affiliated with the Politics Department, the Office of Population Research, the Princeton Institute for Computational Science and Engineering, and the Center for the Digital Humanities. His work develops new quantitative statistical methods for applications across computational social science. He completed his PhD in Government at Harvard in 2015. His work develops new tools for automated text analysis.
Taylor Brown is a doctoral student in the Duke Sociology department, and is associated with the Duke Network Analysis Center. She has a general fascination with computational methods and the issues that arise with social media and other found data. She holds an MA in sociology from UNC-Chapel Hill and an MSc in evidence-based social intervention from the University of Oxford. Prior to beginning her PhD, Taylor worked on issues of intercountry adoption abuse and for a non-profit in Ghana. She also fulfilled an appointment at the National Science Foundation in the division of Social and Economic Sciences.
Shuang (Yo-Yo) Chen is a doctoral student in demography and social policy at Princeton University. Previously, she worked as a consultant at Oxford Policy Management and a program officer for the International Household Survey Network/Accelerated Data Program, providing technical assistance to statistical offices in developing countries. She has also consulted for the World Bank on education projects. She holds a master’s degree in international education policy analysis and a bachelor’s degree in mathematics with honors in education from Stanford University.
As we discussed in our call for applications, we have arranged two types of training prior to the event this summer. Some students have more sophisticated coding skills but little exposure to social science; other students have significant exposure to social science but lack strong coding skills.
The majority of the coding work presented at the 2017 SICSS will employ R. However, you are welcome to employ a language of your choice- such as Python, Julia, or other languages that are commonly used by computational social scientists. If you would like to work in R, we recommend that you complete the following courses within DataCamp, a website that teaches people how to code. Obviously, you only need to complete the classes with material that you would like to learn.
Our institute will bring together people in more than 10 different scholarly fields, some of which are closer to social science than others. For those students with little or no exposure to sociology, economics, or political science, we have assembled a reading list which we ask that you complete prior to the event:
This list includes readings in each of the core areas addressed by the Russell Sage Foundation. Neither your work nor the work we develop together at the institute need map neatly onto these categories, but we think that if those with less exposure to social science read these, we will increase the chances of interdisciplinary cross-pollination, which we view as critical to the future of computational social science.
Finally, we also ask that you read Matt’s book: Bit by Bit: Social Research in the Digital Age. Much of this book will be review for most of you, but if we all read this book ahead of time, then we can use our time together for more advanced topics.
What is Computational Social Science?
Ethics: Principles-based approach
Four areas of difficulty: informed consent, informational risk, privacy, and making decisions in the face of uncertainty
Dinner & discussion
What is big data?
Strengths and weakness of big data
Application Programming Interfaces
Dinner & Discussion
Network-Based Text Analysis
Dinner & Discussion
Introduction to total survey error
Probability and non-probability sampling
New approaches to measurement
Linking surveys and big data
Developing apps for survey research
Dinner & Discussion
Moving beyond simple experiments
Four strategies for experiments
Dinner & Discussion
Why mass collaboration?
Distributed data collection
Dinner & Discussion
Lecture by Deborah Estrin
Lecture by Winter Mason