Helsinki

Pre-arrival

Coding

The majority of the coding work presented at the 2018 SICSS Helsinki Satellite 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 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.

• https://www.datacamp.com/courses/free-introduction-to-r
• https://www.datacamp.com/courses/intermediate-r
• https://www.datacamp.com/courses/data-visualization-with-ggplot2-1
• https://www.datacamp.com/courses/data-visualization-with-ggplot2-2
• https://www.datacamp.com/courses/dplyr-data-manipulation-r-tutorial
• https://www.datacamp.com/courses/cleaning-data-in-r
• https://www.datacamp.com/courses/writing-functions-in-r
• https://www.datacamp.com/courses/reporting-with-r-markdown
• https://www.datacamp.com/courses/exploratory-data-analysis-in-r-case-study
• https://www.datacamp.com/courses/introduction-to-git-for-data-science

We thank DataCamp for making these materials avaialble to admitted participants though their DataCamp for the Classroom program.

Introductions to computational social science

• Grimmer, J. (2015). We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together. PS: Political Science & Politics, 48(01), 80–83. https://doi.org/10.1017/S1049096514001784
• Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), https://doi.org/10.1177/2053951714528481
• Matthew Salganik: Bit by Bit: Social Research in the Digital Age

Readings to gain understanding of social science concepts

For students with little or no exposure to sociology, economics, or political science, we have assembled a collection of exemplary papers demonstrating key concepts and terms. Neither your work nor the work we develop together at the institute does not always build on these concepts, 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.

• Lapinski, M. K. (2005). An Explication of Social Norms. Communication Theory, 15(2), 127–147. https://doi.org/10.1093/ct/15.2.127
• Vargo, C. J., & Hopp, T. (2017). Socioeconomic Status, Social Capital, and Partisan Polarity as Predictors of Political Incivility on Twitter. Social Science Computer Review, 35(1), 10–32. https://doi.org/10.1177/0894439315602858
• Granovetter, M. S. (1973). The Strength of Weak Ties. American Journal of Sociology 78(6). https://www.jstor.org/stable/2776392
• Gamson, W. A., & Lasch, K. E. (1983). The Political Culture of Social Welfare Policy. Evaluating the Welfare State Social and Political Perspectives, 95(221), 397–415. https://doi.org/10.1016/B978-0-12-657980-2.50032-2 a preprint
• Coleman, J. S. (1986). Social Theory, Social Research, and a Theory of Action. American Journal of Sociology 91(6). http://www.jstor.org/stable/2779798