As the participants gear up for the 2019 Summer Institute in Computational Social Science (SICSS) starting June 16th at Princeton and the 11 alumni-led partner locations situated right across the globe, we caught up with the founders of the SICSS, Chris Bail and Matt Salganik, to find out how it all got going, the move to a data intensive society and the benefits of learning data science skills that enable social scientists to analyze this new data.
Don’t forgot, most of the SICSS will be live-streamed so wherever you are in the world you can tune in! You can find the live-stream link here.
SICSS launched in 2017. How did this come about?
We both began our careers doing research and teaching in computational social science (CSS), and we noticed that so many people were struggling to get the training they needed to take advantage of all the great opportunities that now exist. The Russell Sage Foundation has a long tradition of supporting summer schools in interdisciplinary fields and more recently they began supporting computational social science. Through our conversations with them, SICSS was created.
Do you have any anecdotes about SICSS successes that are particularly enlightening for someone who hasn’t attended?
There have been a number of published papers that began at SICSS. But, one great measure of our success is the community that SICSS creates. The feeling at the events is great, and we are so excited about all the alumni-led partner locations happening around the world.
You’re both from a sociological background. Sociologists have played a large part in the emergence of CSS. Why do you think this is?
Sociologists are in a great position to take advantage of the transition to the digital age because it enables us to address core questions in our field in new ways. Also, sociology is an eclectic discipline that is always open to new ideas.
How did you both get started with computational methods and what advice would you give to anyone who is thinking or just starting to learn a programming language?
I (Matt) got started with what we now call computational social science in graduate school. I (Matt) was immensely lucky to be part of a research group (led by my advisor Duncan Watts) that created a community of like-minded people who taught and learned from each other. For people just getting started, I (Matt) would recommend finding a problem you really want to solve and then finding a community that can help you as you struggle through the learning process.
The SICSS mixes students who are more proficient in data science skills with those whose focus has typically been more traditional social science. How integral is this to the success of the summer institute and can you give an example of how the participants different levels of experience have helped during a group project?
The blending of people from different backgrounds is so important, because the blend of ideas is so important; that’s one of the main themes of Matt’s book Bit by Bit: Social Research in the Digital Age. For SICSS in particular, we think that a lot of learning probably happens with participants teaching each other. Working with people from different fields can be hard at first so having time together inside the classroom and out is really critical.
In that vein, is it better to have a research question in mind, and then determine the best method to answer it, or is there use in having a great new tool and looking for questions it might address?
Both approaches can be effective. During the participant-led group research projects that happen during week two of SICSS, we often encourage groups to switch back and forth between these two approaches as the search for a project. No matter how you start, however, you always need to end up with a compelling question.
Speakers at SICSS are not limited to academia—past speakers have been from Facebook, Microsoft and this year will feature the Chief Data Scientist at the New York Times, Chris Wiggins. Why is it important to spotlight CSS research happening in the private sector and can academia and private industry collaborate from effectively?
It is important to highlight CSS research no matter where it is happening. Researchers in companies, governments, and universities often have different opportunities and constraints, and participants benefit from seeing a range of perspectives. More generally, we think that building better collaborations between companies, governments, and universities is important for the long term interest of CSS and society.
You welcome applicants from disciplines that are under-represented in CSS. What fields would you like to see engage more with computational methods?
At this point, we think there is room for growth in every field.
What are the benefits for learning data science skills?
We’ve moved from a world where data is scarce to a world where data is plentiful. If researchers want to take advantage of all of that data, then data science skills are really helpful.
A great success story of the SICSS are the growing number of partner locations run by the alumni of 2017 and 2018. Is this something you always planned for future years?
The partner locations were a complete accident. In 2017 we were excited to receive so many outstanding applicants, and it was heartbreaking to reject so many of them. During SICSS 2017 it became clear that a huge part of SICSS was going through the experience as part of a community, and we started to think about how we could scale that up. One of our guest speakers, Debroah Estrin, actually suggested the idea of alumni-run partner locations, and we loved it. We are grateful to the Alfred P. Sloan Foundation for supporting the partner locations. There were seven in 2018 and 11 in 2019.
Can you highlight any research has been made possible by participation in the summer institutes?
Participant-driven, collaborative research projects are a big part of the Summer Institute. Participants learn a lot from these projects and really enjoy them. We are especially happy that participants often continuing working on the projects long after the Summer Institute ends. Three projects that started at SICSS 2017 are currently in press, and as time goes on we expect that some of the projects started at SICSS will be published soon.
Do you want to learn or teach computational social science? Later in the summer Matt and Chris will release the teaching and learning materials from this year’s Summer Institute. You can find all the resources here. You can also follow Chris and Matt on Twitter for the latest news and updates.
Look out for more content from the Summer institute on our blog over the coming weeks.
Chris Bail is Professor of Sociology and Public Policy at Duke University where he directs the Polarization Lab. He is also affiliated with the Interdisciplinary Data Science Program, the Duke Network Analysis Center, and the Duke Population Research Institute. His research examines political polarization, culture and social psychology using tools from the field of computational social science. He is the author of Terrified: How Anti-Muslim Fringe Organizations Became Mainstream.
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 Bit by Bit: Social Research in the Digital Age.