Goal

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:

  1. computational thinking, here understood as the process where real world problem is presented as a coding problems
  2. basics of Python-language to be able to implement solutions to some of the real world problems
  3. applications of computational social science in practice, that is existing literature and different applications they have in social science

Course practices

We have about three hours on Thursday evenings (16-19, expect 20.11. and 27.11. on Fridays). Each class we start together discussing about the articles and common tasks. We it is possible to do exercises and Matti is there to help you with them. At the end of the day, Matti will shortly summarise the main programming topics this and next week.

We have a Facebook-group to share to share knowledge and ask questions.

We use flipped classroom (see: Wikipedia) 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. These exercises can be done in ones own phase throughout the course. The programming exercises are categorised to groups based on the content

  • variables and operations
  • control structures
  • function
  • data structures
  • applied exercises.
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.

Articles present more high level discussion around computational social science. These articles present the background and history of computational social science, critical perspectives regarding them and methods descriptions. Each student must read these articles and prepare to discuss them to every class. To support this, we provide some instructive questions on these papers, but our discussions are not limited on them only.

Often it is most useful to think what the paper means for you and your domain of expertise.

Evaluation

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

  • 60% of exercises completed for each exercise group
  • 75% of exercises done for all exercises
  • participation on the papers discussions or completing them in other ways

Articles for everyone to read

  1. Why we are teaching science wrong, and how to make it right
    • What is active learning?
  2. Cioffi-Revilla, Claudio. 2010. “Computational Social Science.” Wiley Interdisciplinary Reviews: Computational Statistics 2 (3): 259–271.
    • Check that you understand each of the tools presented by Cioffi-Revilla in the article
    • Which of these tools is most suitable to your research?
  3. Lazer et al. (2009): Life in the network: the coming age of computational social science
    • Explain why Lazer et al. see the need for computational social science
    • What are the differences between Cioffi-Revilla and Lazer et al.?
    • Which paper the two papers is better for you and your research?
  4. Gillespie, Tarleton (2014): The Relevance of Algorithms. In Media Technologies: Essays on Communication, Materiality, and Society, 167–194.
    • What are sociologists of algorithms and how are they relevant in your field?
    • Which of the challenges presented in this work is most relevant for your research?
  5. boyd, danah, and Kate Crawford. 2012. “Critical Questions for Big Data.” Information, Communication & Society 15 (5): 662–679.
    • What is big data and how it is relevant in your field?
    • Which of the challenges presented in this work is most relevant for your research?
  6. CLRS: Introduction to Algorithms pages 5-15 (skip problems) and 23-28.
    • What is the key message authors have?
    • Can you order of growth -approach in your research?
  7. Grimmer, Justin, and Brandon M. Stewart. 2013. “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts.” Political Analysis 21: 267–297.
    • What is the key message authors have?
    • Can you apply text as data -approach in your research?

Case studies

For each case study, prepare to discuss the research question they had and the computational method they applied. Also, position the computational method to Cioffi-Revilla's list of approaches.

Do not focus on the results of the works, we are not interested on those!

  1. 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).
  2. 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.
  3. 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
  4. 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).
  5. 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.
  6. 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.

Python-material

The Web is full of resources! Codeacademy has pretty good interactive material. Also, following chapters and sections, hand-picked from various online sources, specially address topics of our course.

Exercises

Exercises

Mark exercises

Python environment

I strongly recommend, that you work in the order proposed in the "Mark exercises" sheet. If the exercise is written in blue(ish) color, kindly sent it to Matti (matti.nelimarkka@helsinki.fi) to get 1-to-1 feedback of it. You can send them in separate files or as collection of sets.

Examples

More info

Kindly see the frequently asked questions before contacting Matti.

Please contact Matti Nelimarkka (matti.nelimarkka@helsinki.fi).

For personal tutoring, see the online calendar and choose a time and date.

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