This is a seminar course. By reading and discussing important papers from computational social science, students will become familiar with core issues and techniques in the field. Students will also propose and implement individual research projects, which can potentially lead to publications.


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 by reading, discussing, and extending important papers from computational social science. Every week, we will focus on one paper. All students will write a short summary and review of the paper, and one student will lead the in-class discussion. Later in the semester, students will propose and implement individual projects, which can potentially lead to publications in workshops or conferences.

Beyond familiarizing themselves with research in the field, students will become better at assessing and critiquing scholarly work (by discussing and reviewing papers) and at identifying and implementing novel research questions (through the course project).


  • Official webpage
  • Time: Mondays, 11:15 - 13:00, March 20 – May 29, 2017
  • Location: BC 04
  • Everybody is welcome to join us as a guest in reading and discussing the papers listed below!


Week 1 (2/20): Introduction

Discussion lead: Bob West

  • David Lazer, Alex Pentland, Lada Adamic, Sinan Aral, Albert-László Barabási, Devon Brewer, Nicholas Christakis, Noshir Contractor, James Fowler, Myron Gutmann, Tony Jebara, Gary King, Michael Macy, Deb Roy, and Marshall Van Alstyne: Computational Social Science, Science, 2009. [PDF] (very short paper; no review to be written)
  • Andrew Tomkins, Min Zhang, and William D. Heavlin: Single versus Double Blind Reviewing at WSDM 2017, ArXiv preprint, 2017. [PDF] (no review to be written)

Week 2 (2/27): Large-scale social experiments

Discussion lead: David Aksun

  • Jeffrey Travers and Stanley Milgram: An Experimental Study of the Small World Problem, Sociometry, 1969. [PDF] (no review to be written)
  • Peter Sheridan Dodds, Roby Muhamad, and Duncan J. Watts: An Experimental Study of Search in Global Social Networks, Science, 2003. [PDF]

Week 3 (3/6): Observational studies: drawing valid conclusions from “found data” (part 1)

Discussion lead: Sai Praneeth Reddy

  • Derek Ruths and Jürgen Pfeffer: Social Media for Large Studies of Behavior, Science, 2009. [PDF] (no review to be written)
  • Peter C. Austin: An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies, Multivariate Behavioral Research, 2011. [PDF]

If you want more background on observational studies, propensity scores, etc., this book is a fantastic and easy-to-grasp resource (the book is, of course, not required reading…): Paul R. Rosenbaum: Design of Observational Studies, Springer, 2010. [PDF]

Week 4 (3/13): Online communication behavior

Discussion lead: Julia Proskurnia

  • Farshad Kooti, Luca Maria Aiello, Mihajlo Grbovic, Kristina Lerman, and Amin Mantrach: Evolution of Conversations in the Age of Email Overload, Proc. World Wide Web Conference, 2015. [PDF]

Week 5 (3/20): Network analysis of social systems

Discussion lead: Mia Primorac

  • Aaron Clauset, Samuel Arbesman, and Daniel B. Larremore: Systematic Inequality and Hierarchy in Faculty Hiring Networks, Science Advances, 2015. [PDF]

Week 6 (3/27): Language and social phenomena

Discussion lead: Victor Kristof

  • Daniel Casasanto, Kyle Jasmin, Geoffrey Brookshire, and Tom Gijssels: The QWERTY Effect: How Typing Shapes Word Meanings and Baby Names, Proc. Annual Conference of the Cognitive Science Society, 2014. [PDF]
  • David Garcia and Markus Strohmaier: The QWERTY Effect on the Web: How Typing Shapes the Meaning of Words in Online Human-Computer Interaction, Proc. World Wide Web Conference, 2016. [PDF] (optional reading)

Week 7 (4/3)

Bob away in Australia at the WWW conference. Students use this week for preparing project proposals. Project proposals due at the end of the week.

Week 8 (4/10): Opinions on the Web

Discussion lead: Tiziano Piccardi

  • Justin Cheng, Michael Bernstein, Cristian Danescu-Niculescu-Mizil, and Jure Leskovec: Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions, Proc. Conference on Computer-Supported Cooperative Work and Social Computing, 2017. [PDF]

Students get feedback on proposals by the end of the week.

Week 9 (4/17)

Easter week. Students use this week for adapting proposals based on Bob’s feedback and for preparing lightning proposal talks.

Week 10 (4/24)

Lightning proposal talks.

Week 11 (5/1): Observational studies: drawing valid conclusions from “found data” (part 2)

Discussion lead: Leo Impett

  • Gary King and Richard Nielsen: Why Propensity Scores Should Not Be Used for Matching, Working paper, 2016. [PDF]

Week 12 (5/8): Online versus offline behavior

Discussion lead: Pierre Runavot

  • Tim Althoff, Eric Horvitz, Ryen W. White, and Jamie Zeitzer: Harnessing the Web for Population-Scale Physiological Sensing: A Case Study of Sleep and Performance, Proc. World Wide Web Conference, 2017. [PDF]

Week 13 (5/15): Machine learning and data mining for social systems

Discussion lead: Jérémie Rappaz

  • Ruining He and Julian McAuley: Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering, Proc. World Wide Web Conference, 2016. [PDF]

Week 14 (5/22)

No meeting.

Week 15 (5/29): Ethics of computational research on human behavior

Discussion lead: Fu-Yin Cherng

  • Adam D. I. Kramer, Jamie E. Guillory, and Jeffrey T. Hancock: Experimental Evidence of Massive-scale Emotional Contagion through Social Networks, Proc. National Academy of Sciences, 2014. [PDF]

Additional material about the study (e.g., media coverage in the aftermath) is available here. Please do take a look!