Istanbul

Pre-arrival

Reading List (for everyone)

The Summer Institute 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 Matt’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 we’d like you to read following articles

• 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
• Wallach, H. (2018). Computational social science ≠ computer science + social data. Communications of the ACM, 61(3), 42–44. https://doi.org/10.1145/3132698

Coding (for non-coders)

The majority of the coding work presented at the 2019 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 are unfamiliar with programming, we recommend R for synenergy benefits. You can learn R from the free RStudio Primers, which can be supplemented by the open access book R for Data Science by Garrett Grolemund and Hadley Wickham. RStudio Primers cover 6 topics: The Basics, Working with Data, Visualize Data, Tidy Your Data, Iterate, and Write Functions. If you already feel comfortable with these topics (either in R or some other language), then you do not need to complete these Primers.

If you would like more practice after completing the RStudio Primers, some other materials that we can recommend are:

• R for Social Science, Data Carpentry.
• Introduction to R for Social Scientists, Taught by Charles Lanfear at University of Washington. This course includes videos of lectures, slides, and assignments.
• Learn R, Code Academy.

Reading List (for non-social scientists)

Also, for students with little or no exposure to sociology, economics, or political science, we have assembled a collection of exemplary papers which introduce social science thinking:

• 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
• Gamson, W. A. (1989). Media Discourse and Public Opinion on Nuclear Power: A Constructionist Approach. American Journal of Sociology, 95(1), 1–37. https://doi.org/http://doi.org/10.1086/229213