From the evening of Sunday, July 28 to Friday, August 09, 2019, the University of Bamberg will host a partner event for the Summer Institute in Computational Social Science. The purpose of SICSS Bamberg is to bring together interested Master students, PhD students, postdoctoral students and faculty in Germany. The Summer Institute is for both social scientists (broadly conceived) and data scientists (broadly conceived).
The instructional program will involve lectures and group problem sets in the first week and participant-led research projects in the second week. Participants will be able to work in teams to learn how to implement the material from the lectures. Covered topics include ethics, web scraping and collecting digital trace data, automated text analysis, network analysis, digital field experiments, and surveys in the digital age. There will be ample opportunities for students to discuss their ideas and research with the organizers, other participants, and visiting speakers. One goal of this partner location is to build and expand the network of computational social scientists in Germany.
We are inviting applications from Master students, PhD students, postdoctoral researchers, and untenured faculty within 5 years of their PhD. Participants are expected to fully attend and participate in the entire two-week program. Participants with less experience with social science research will be expected to complete additional readings in advance of the Institute, and participants with less coding experience will be expected to complete a set of online learning modules on the R programming language. There is no cost for participating in the Summer Institute. For a limited number of participants, we will be able to cover housing and meal expenses up to a set cap.
Application materials are due on Monday, February 25, 2019.
We thank the Bamberg Graduate School of Social Sciences (BAGSS) for the generous support of this SICSS partner event.
We are also grateful for financial support from the Russell Sage Foundation and the Alfred P. Sloan Foundation.
All events will be held at:
University of Bamberg
Carsten Schwemmer is currently finishing a PhD in Sociology at Bamberg University, Germany. His research focuses on computational methods for the study of ethnic minorities and social media communication. Carsten is particularly interested in natural language processing, data mining and software development. He gave courses on computational social science at University of Bamberg, University of Constance and Humboldt University of Berlin.
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 coding skills.
The majority of the coding work presented at the 2019 SICSS Bamberg will employ R. However, you are welcome to employ a language of your choice, such as Python, 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 with courses on many topics related to data science. Obviously, you only need to complete the classes with material that you would like to learn.
We thank DataCamp for making these materials available to admitted participants though their DataCamp for the Classroom program.
The Summer Institute in Bamberg will bring together people from many fields, and therefore we think that asking you to do some reading before you arrive will help us use our time together more effectively. First, we ask you to read Matthew Salganik’s book, Bit by Bit: Social Research in the Digital Age (Read online or purchase from Amazon, Barnes & Noble, IndieBound, or Princeton University Press), which is a broad introduction to computational social science. Parts 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.
Also, for students with little or no exposure to sociology, economics, or political science, we have assembled a collection of exemplary papers. Neither your work nor the work we develop together at the institute need map neatly onto these categories, but 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.
The schedule will be posted in the coming weeks.