By the end of the course, you can write small Python code "snippets" to collect and process data. For this purposes, we have three different goals for the course:
We have about three hours on Friday mornings. The first hour (8.15-9.00) is reserved for students to work on the exercises and receive help and instructions from Matti. The next hour, 9.00-10.00 is time for all students, engaging into discussion about the articles and reviewing common challenges. This in most cases will take less than one hour, but kindly reserve a hour timeslot for this. Following this, 10.00-10.45 is again reserved for tutoring and working on the problem exercises together.
We have a Facebook-group to share to share knowledge and ask questions.
We use flipped classroom approach in this course. That means we expect students to familiarize with the course material, that is the assigned papers and materials regarding Python, on their own before lectures. During the lectures, we discuss and explore the assigned papers together and provide time to work on the exercises.
Programming exercises train computational thinking and Python programming in practice. Exercises include closed-ended programming exercises, but in the end, we move to applied open-ended exercises. To struture the course, each exercise set has a deadline, shown in the exercise system. The programming exercises are categorized into groups based on the content
To estimate the workload of the course, expect each group to have 20 - 30 exercises.
Case studies present examples of recent research applying computational social science. Each case is studied in groups, thus each student reads and presents one paper and engages in group discussions on other papers.
The course is graded on pass/fail -scale. This aims to motivate everyone to take the course, as it will not impact your grade point average.
Passing grade on this course requires
As such, the course has no mandatory prerequisites. However, if you have not taken the Introduction to Computational Social Science, I strongly encourage you to read following materials. There are some questions to help you capture the main issues.
For each case study, prepare to discuss the research question they had and the computational method they applied. For each article, position the computational method to Cioffi-Revilla's list of four approaches, identify how results were validated, and discuss the ethical aspects of the research. Finally, identify tools and frameworks they use in that research. Focus on the methods, not the results. This is a methods class and the articles may be somewhat boring results wise.
Things to cover
Reading: Why we are teaching science wrong, and how to make it right
Article: Shor, Eran, Arnout van De Rijt, Charles Ward, Soussan Askar, and Steven Skiena. 2014. “Is There a Political Bias? A Computational Analysis of Female Subjects’ Coverage in Liberal and Conservative Newspapers.” Social Science Quarterly 95 (5).
Article: Adamic, Lada A, and Natalie Glance. 2005. “The Political Blogosphere and the 2004 U.S. Election: Divided They Blog.” In Proceedings of the 3rd International Workshop on Link Discovery, 36–43.
Set 1 deadline
Article: Pearson, J., R. Lay-Yee, P. Davis, D. O’Sullivan, M. von Randow, N. Kerse, and S. Pradhan. 2010. “Primary Care in an Aging Society: Building and Testing a Microsimulation Model for Policy Purposes.” Social Science Computer Review 29 (1) (May 18): 21–36
Set 2, 3 and 4 deadlines.
CLRS: Introduction to Algorithms pages 5-15 (skip problems) and 23-28.
Article: Flaounas, Ilias, Omar Ali, Thomas Lansdall-Welfare, Tijl De Bie, Nick Mosdell, Justin Lewis, and Nello Cristianini. 2013. “Research Methods in the Age of Digital Journalism. Massive-Scale Automated Analysis of News-Content-Topics, Style and Gender.” Digital Journalism 1 (1).
Set 5, 6 and 7 deadlines.
Dec 12t - support available 8-10 and 12-14.
Levy, K. E. C., and M. Franklin. 2013. “Driving Regulation: Using Topic Models to Examine Political Contention in the U.S. Trucking Industry.” Social Science Computer Review. Burscher, B., R. Vliegenthart, and C. H. de Vreese. 2015. “Frames Beyond Words: Applying Cluster and Sentiment Analysis to News Coverage of the Nuclear Power Issue.” Social Science Computer Review (1991): 1–16.
Set 8 and 9 deadlines