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.
As part of the class, enrolled students will write what we call synthesis proposals. Students will choose one paper from computer science or a related field 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.
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 can be really good for getting discussions going, clarifying things, etc. Let’s give this a try!
- Official webpage
- Time: Tuesays, 10:15 - 12:00, February 19 – May 28, 2019
- 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 (even on a paper-by-paper basis)!
- Weekly themes and chapter numbers refer to the Bit by Bit book.
- Weekly readings include the book chapter 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 19)
Introduction and logistics [slides]
Week 2: Observing behavior (chapter 2) (February 26)
Discussion leader: Akhil Arora
- Amit Sharma, Jake M. Hofman, Duncan J. Watts: Estimating the causal impact of recommendation systems from observational data, Proceedings of the 16th ACM Conference on Economics and Computation, 2015.
Week 3: Observing behavior (chapter 2) (March 5)
Discussion leader: Kristina Gligorić
- Sander Greenland, Judea Pearl, James M. Robins: Causal diagrams for epidemiologic research, Epidemiology 10:37-48, 1999.
- Chapters 4 and 7 of Judea Pearl, Dana Mackenzie: The Book of Why, Basic Books, 2018.
Week 4: Observing behavior (chapter 2) (March 12)
Discussion leader: Maximilian Hofer
- 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.
- Chapters 1 and 3 of Paul R. Rosenbaum: Design of Observational Studies, Springer, 2010. (No review required.)
Week 5: Asking questions (chapter 3) (March 19)
Discussion leader: Georgia Fragkouli
- Wei Wang, David Rothschild, Sharad Goel, Andrew Gelman: Forecasting elections with non-representative polls, International Journal of Forecasting 31(3):980-991, 2015.
Week 6: Asking questions (chapter 3) (March 26)
Discussion leader: Marc-Antoine Coindreau
- Joshua Blumenstock, Gabriel Cadamuro, Robert On: Predicting poverty and wealth from mobile phone metadata, Science 350(6264):1073-1076, 2015. (Skim the supplemental material.)
Week 7: Running experiments (chapter 4) (April 2)
Discussion leader: Aleksandra Petersone
- Christopher A. Bail, Lisa P. Argyle, Taylor W. Brown, John P. Bumpus, Haohan Chen, M. B. Fallin Hunzaker, Jaemin Lee, Marcus Mann, Friedolin Merhout, Alexander Volfovsky: Exposure to opposing views on social media can increase political polarization, Proceedings of the National Academy of Sciences 115(37):9216-9221, 2018. (Skim the supplemental material.)
Week 8: Running experiments (chapter 4) (April 9)
Discussion leader: George Abi Younes
- Ravi Bapna, Jui Ramaprasad, Galit Shmueli, Akhmed Umyarov: One-way mirrors in online dating: A randomized field experiment, Management Science 62(11):3100-3122, 2016.
- Chapter 2 of Paul R. Rosenbaum: Design of Observational Studies, Springer, 2010. (No review required.)
Week 9: Running experiments (chapter 4) (April 16)
Discussion leader: Ye Wang
- Dan Ariely, George Loewenstein, Drazen Prelec: “Coherent arbitrariness”: Stable demand curves without stable preferences, The Quarterly Journal of Economics 118(1):73-106, 2003.
- Chapters 1 and 2 of Dan Ariely: Predictably Irrational, Harper, 2008. (Fun optional reading; no review required.)
Week 10 (April 23)
No meeting (Easter week)
Week 11: Creating mass collaboration (chapter 5) (April 30)
Discussion leader: Mark Sutherland
- Galen Pickard, Wei Pan, Iyad Rahwan, Manuel Cebrian, Riley Crane, Anmol Madan, Alex Pentland: Time-critical social mobilization, Science 334(6055):509-512, 2011. (Skim the supplemental material.)
- John C. Tang, Manuel Cebrian, Nicklaus A. Giacobe, Hyun-Woo Kim, Taemie Kim, Douglas Beaker Wickert: Reflecting on the DARPA red balloon challenge, Communications of the ACM 54(4):78-85, 2011. (No review required.)
- Alex Rutherford, Manuel Cebrian, Sohan Dsouza, Esteban Moro, Alex Pentland, Iyad Rahwan: Limits of social mobilization, Proceedings of the National Academy of Sciences, 201216338, 2013. (Optional reading; no review required.)
Week 12: Creating mass collaboration (chapter 5) (May 7)
Discussion leader: Trung Phan
- Ei Pa Pa Pe-Than, Dion Hoe-Lian Goh, Chei Sian Lee: A typology of human computation games: An analysis and a review of current games, Behaviour & Information Technology 34(8):809-824, 2015.
- Luis von Ahn, Laura Dabbish: Designing games with a purpose, Communications of the ACM 51(8):58-67, 2008. (Optional reading; no review required.)
Week 13 (May 14)
No meeting (The Web Conference)
Week 14: Ethics (chapter 6) (May 21)
Discussion leader: Efstratios Triantafyllou
- Kevin Lewis, Jason Kaufman, Marco Gonzalez, Andreas Wimmer, Nicholas Christakis: Tastes, ties, and time: A new social network dataset using Facebook.com, Social Networks 30(4):330-342, 2008.
- Michael Zimmer: “But the data is already public”: On the ethics of research in Facebook, Ethics and Information Technology 12(4):313-325, 2010. (Optional reading; no review required.)
Week 15 (May 28)
Presentations of synthesis proposals