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This is a seminar course. By reading and discussing an introductory book as well as research papers about computational social science, students will become familiar with core issues and techniques in the field.


Data collected through digital systems, such as online social networks, search engines, mobile phones, apps, etc., offer great opportunities for addressing important research questions about individual as well as collective human behavior. Whereas such issues had previously been studied primarily by social scientists, the sheer size of modern social data sets, as well as the fact that they are produced within computational systems, requires computational ways of thinking about, and processing, them.

The goal of this seminar is to acquaint students with some of the fundamental questions and techniques arising in the context of computational social science.

We will explore the above topics simultaneously in two ways:

  • We will read the book Bit by Bit: Social Research in the Digital Age by Matthew Salganik (available online for free).
  • We will read research papers from computational social science that provide a deep dive into the topics(s) discussed in the book.

Every week, we will focus on one book chapter and one accompanying paper (and sometimes additional complementary materials). All students will write a short summary and review of the respective paper, and one student will lead the in-class discussion, which will be about the paper as well as the book chapter etc. Beyond familiarizing themselves with research in the field, students will become better at assessing and critiquing scholarly work (by discussing and reviewing papers).

Through this course, students will obtain an overview of the research questions posed in computational social science, and of the tools and techniques available. Moreover, they will increase their ability to summarize and critique scientific papers.

Previous years: 2019, 2018, 2017.

Note: In previous years, students wrote and presented 3-page “synthesis proposals”. This year, this is not the case anymore.

Collaborative annotation

If you click on the paper links below, you’ll see that there’s a side bar provided by the Web annotation service Hypothes.is. This side bar allows you to comment on any part of the respective paper. It may help with getting discussions going, clarifying things, etc. Let’s give this a try!


  • Coursebook listing
  • Time: Tuesays, 10:15 – 12:00, February 18 – May 26, 2020
  • Location: INM 11
  • Paper reviews should be submitted via EasyChair by Saturday 23:59
  • Everybody is welcome to join us as a guest in reading and discussing the papers listed below (for as many or few papers as they like)!


  • Weekly themes and chapter numbers refer to the Bit by Bit book.
  • Weekly readings include the book chapter from Bit by Bit and the weekly paper (and additional materials where indicated). Chapters should be read fully by the first week in which a chapter is discussed.
  • Discussion leaders should guide a discussion about the paper as well as the more general context provided by the book chapter (and any additional material they deem worthy of talking about).

  • To make things easier, here are all papers in a single zip file.

Week 1 (February 18)

Introduction and logistics [slides]

Week 2: Observing behavior (chapter 2) (February 25)

Discussion leader: Manoel Horta Ribeiro

Week 3: Observing behavior (chapter 2) (March 3)

Discussion leader: Juan Carlos Farah

Also read chapter 4 of Judea Pearl, Dana Mackenzie: The Book of Why, Basic Books, 2018 (no review required).

Week 4: Observing behavior (chapter 2) (March 10)

Discussion leader: Bogdan Kulynych

Also read chapter 5 of Paul R. Rosenbaum: Observation and Experiment, Harvard University Press, 2017 (no review required).

Week 5: Observing behavior (chapter 2) (March 17)

Discussion leader: Beatriz Borges

Also read chapter 6 of Paul R. Rosenbaum: Observation and Experiment, Harvard University Press, 2017 (no review required).

Week 6: Observing behavior (chapter 2) (March 24)

Discussion leader: Ling Zhou

Week 7: Asking questions (chapter 3) (March 31)

Discussion leader: Christoph Finkensiep

Week 8: Running experiments (chapter 4) (April 7)

Discussion leader: Sascha Nick

Week 9 (April 14)

No meeting (Easter week)

Week 10 (April 21)

No meeting (The Web Conference)

Week 11: Running experiments (chapter 4) (April 28)

Discussion leader: Victor Salvia

Also read chapters 1 and 2 of Paul R. Rosenbaum: Observation and Experiment, Harvard University Press, 2017 (no review required).

Week 12: Running experiments (chapter 4) (May 5)

Discussion leader: Alexis Gumy

Also read chapters 3 and 4 of Paul R. Rosenbaum: Observation and Experiment, Harvard University Press, 2017 (no review required).

Week 13: Creating mass collaboration (chapter 5) (May 12)

Discussion leader: Jessica Pidoux

Week 14: Creating mass collaboration (chapter 5) (May 19)

Discussion leader: André Ghattas

Week 15: Ethics (chapter 6) (May 26)

Discussion leader: Una Pale

Additionally, please read this short blog post by Duncan Watts about “lessons learned from the Facebook study” (no review required).