This is a seminar course. By commonly reading and discussing an introductory book, as well as important 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 important research papers from computational social science that provide a deep dive into the topics discussed in the book.

Every week, we will focus on one book chapter and one accompanying paper. All students will write a short summary and review of the respective paper, and one student will lead the in-class discussion. 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 important 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.

Synthesis proposals

As part of the class, enrolled students will write what I call synthesis proposals. Students will choose one paper from computer science or a related field (e.g., social computing, social network analysis, natural language processing, data mining, machine learning, human–computer interaction, etc.) and will discuss in a short document (3 pages) how that paper could be improved or enriched with the computational social science techniques we have encountered in class. At the end of the semester, students will also present their synthesis proposals in short talks.

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 This side bar allows you to comment on any part of the respective paper. It can be really good for getting discussions going, clarifying things, etc. Let’s give this a try!


  • Official webpage
  • Time: Mondays, 10:15 - 12:00, February 19 – May 28, 2018
  • Location: BC 02
  • 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!


  • Weekly themes and chapter numbers refer to the Bit by Bit book.
  • Weekly readings include the book chapter and the weekly paper. Chapters should be read fully by the first week in which a chapter is discussed.
  • Discussion leaders should make the discussion mostly about the paper, but also make sure to position the paper in the broader context of the entire book chapter.

Week 1 (February 19)

Introduction and logistics

Week 2: Observing behavior (chapter 2) (February 26)

Gary King, Jennifer Pan, Margaret E. Roberts: How Censorship in China Allows Government Criticism but Silences Collective Expression, American Political Science Review 107(2):326–343, 2013.

Discussion leader: Greg Ódor

Week 3: Observing behavior (chapter 2) (March 5)

Peter C. Austin: An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies, Multivariate Behavioral Research 46:399–424, 2011.

Discussion leader: Martí Bosch Padros

Week 4: Observing behavior (chapter 2) (March 12)

Florencia Torche, Uri Shwed: The Hidden Costs of War: Exposure to Armed Conflict and Birth Outcomes, Sociological Science 2:558–581, 2015.

Discussion leader: Ramtin Yazdanian

Week 5: Asking questions (chapter 3) (March 19)

Stephen Ansolabehere, Eitan Hersh: Validation: What Big Data Reveal About Survey Misreporting and the Real Electorate, Political Analysis 20 (4):437–59, 2012.

Discussion leader: Farnaz Eslamishoar

Week 6: Asking questions (chapter 3) (March 26)

Sharad Goel, Winter Mason, Duncan J. Watts: Real and Perceived Attitude Agreement in Social Networks, Journal of Personality and Social Psychology 99(4):611–621, 2010.

Discussion leader: Kristina Gligorić

Week 7 (April 2)

No meeting (Easter Monday)

Week 8: Running experiments (chapter 4) (April 9)

Neil Thompson, Douglas Hanley: Science Is Shaped by Wikipedia: Evidence From a Randomized Control Trial, MIT Sloan Research Paper No. 5238-17, 2018.

Discussion leader: Dalia El Badawy

Week 9: Running experiments (chapter 4) (April 16)

Kevin Munger: Tweetment Effects on the Tweeted: Experimentally Reducing Racist Harassment, Political Behavior 39(3):629–649, 2017.

Discussion leader: Solal Pirelli

Week 10 (April 23)

No meeting (The Web Conference)

Week 11: Running experiments (chapter 4) (April 30)

Jennifer L. Doleac, Luke C.D. Stein: The Visible Hand: Race and Online Market Outcomes, Economic Journal 123(572):F469–F492, 2013.

Discussion leader: Dušan Kostić

Week 12: Creating mass collaboration (chapter 5) (May 7)

Ceren Budak, Sharad Goel, Justin M. Rao: Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis, Public Opinion Quarterly 80(S1):250–271, 2016.

Discussion leader: Tuğrulcan Elmas

Week 13: Creating mass collaboration (chapter 5) (May 14)

Goran Radanovic, Boi Faltings, Radu Jurca: Incentives for Effort in Crowdsourcing Using the Peer Truth Serum, ACM Transactions on Intelligent Systems and Technology 7(4):48, 2016.

Discussion leader: Bogdan-Alexandru Stoica

Week 14 (May 21)

No meeting (Pentecost Monday)

Week 15: Ethics (chapter 6) (May 28)

Sam Burnett and Nick Feamster: Encore: Lightweight Measurement of Web Censorship with Cross-Origin Requests, Proceedings of the ACM Conference on Special Interest Group on Data Communication, 2015.

Discussion leader: Blagovesta Hristova Kostova