Instructional staff

Meeting times/locations

Tuesdays/Thursdays 2:00-3:20 pm in Social Sciences Rsch Bldg 401

Course description

Computational Social Science (CSS) combines the theoretical paradigms of the social sciences with the expanded data and computational methods of computer science. Massive digital traces of human behavior and ubiquitous computation have both extended and altered classical social science inquiry. This course surveys successful social science applications of computational approaches to the representation of complex data, information visualization, and model construction and estimation. We will examine the scientific method in the social sciences in context of both theory development and testing, exploring how computation and digital data enables new answers to classic investigations, the posing of novel questions, and new ethical challenges and opportunities. Students will review fundamental research designs and assess how computational opportunities can enhance them. This is a survey course - we will explore the wide range of contemporary approaches to computational social science by reading and critiquing published research papers throughout the fields of economics, political science, psychology, and sociology.

Learning objectives

By the end of this course, students will be able to:

Course structure

Class sessions will consist of a mix of lecture and discussion, giving you the opportunity to learn about computational research design strategies and critique them with your peers.

All course content and readings are organized on Canvas under the “Modules” on the left-hand side of your screen. You should read all of the readings listed for a given class session ahead of class time and be prepared for in-class discussion. I will release each week's module no later than 11:59 pm on the preceding Saturday. 

Our textbook for this course is:

Salganik, Matthew J., Bit by Bit: Social Research in the Digital Age, Princeton University Press, 2018.

In the course calendar below we cite Bit by Bit chapters that correspond to the lesson for the week. 

On each Tuesday, I will also assign a weekly mental exercise and explain it in class. You should spend a significant amount of effort thinking about your answers and getting prepared for Thursday's discussion.  

Evaluation

Grades will be based on your performance on a range of assignments. All the specific instructions for each assignment can be found under the Assignments or Modules tab.

Assessment of observational study vs. experiments (20%)

Due Date: 10/29/2022, 11:59 pm CT

Description:

You will write a paper of around 1800-2200 words comparing and contrasting the usage of an observational study vs. a digital experiment to answer a research question. Specific instructions for the assignment will be provided one week before the due date.

Ethical writing assignment (20%)

Due Date: 11/12/2022, 11:59 pm CT

Description:

You will write a paper of around 1500-2000 words addressing a specific issue related to CSS and ethics. Specific instructions for the assignment will be provided one week before the due date.

Develop a research proposal (40%)

Due Date: 12/8/2022, 11:59 pm CT

For the final assignment, you will write an original research proposal for a social scientific question of your own interest and develop a computationally-enhanced research design to answer the question. Specific instructions for the assignment will be provided three weeks before the due date.

Participation (20%)

A big part of this class involves in-class discussion of the assigned readings, which will drive you to engage deeper with specific computational social science research designs and allow you to practice taking part in scholarly discussions. Participation is evaluated weekly according to a Check+/Check/Check-/Absence system, which converts to 12/10/8/0 out of 10 points per week. Every one of you should try to speak up at least once a week. Your participation grade will be evaluated based on your attendance and contribution. When there are not enough chances for everyone to speak, we will switch to a bi-weekly system. 

Plagiarism and academic honesty

In our first class meeting, we will discuss what constitutes plagiarism and how to avoid it. Academic honesty is an extremely important principle in academia and at the University of Chicago.

Submitting late assignments

All assignments should be submitted by the deadline listed on Canvas. The only exceptions are for religious observances, injury/illness, and similarly exceptional situations. If you are unable to submit an assignment on time and you know in advance, you must contact your instructor prior to the assignment deadline to arrange an extension. If you fall ill or are unable to ask in advance, please contact your instructor as soon as possible. We are generally reasonable in these situations, but you need to let us know.

Revising and resubmitting Midterm Papers

You will be permitted to revise and resubmit each paper to earn back up to 50% of the points deducted in your first attempt, so long as you turned it in on time and made a good-faith effort to write the paper in your first submission. Your revisions must be submitted within 7 days of grades being released for the assignment in question. With your revisions, you should include a cover letter detailing what you changed in your submission and why your revisions address all of the issues with your original paper.

Grading

Grades are not curved in this class or, at least, not in the traditional sense. We use a standard set of grade boundaries:

We curve only to the extent we might lower the boundaries for one or more letter grades, depending on the distribution of the raw scores across sections of this course. We will not raise the boundaries in response to the distribution.

So, for example, if you have a total score of 82 in the course, you are guaranteed to get, at least, a B (but may potentially get a higher grade if the boundary for a B+ is lowered).

If you would like to be graded on a Pass/Fail (P/F) basis, send a message to your instructor before the Final Research Proposal is due. A total score of 75 and above in the class will qualify for a “P” in the class. Note that MACSS and CSS Certificate students must take this course for a letter grade.

Statement on diversity, inclusion, and disability

The University of Chicago is committed to diversity and rigorous inquiry from multiple perspectives. The MAPSS, CIR, and Computation programs share this commitment and seek to foster productive learning environments based upon inclusion, open communication, and mutual respect for a diverse range of identities, experiences, and positions.

The University of Chicago is committed to ensuring equitable access to our academic programs and services. Students with disabilities who have been approved for the use of academic accommodations by Student Disability Services (SDS) and need a reasonable accommodation(s) to participate fully in this course should follow the procedures established by SDS for using accommodations. Timely notifications are required in order to ensure that your accommodations can be implemented. Please meet with your instructor to discuss your access needs in this class after you have completed the SDS procedures for requesting accommodations

Major topics

Specific readings for each week will be listed in Modules.

  1. Introduction to computational social science
  2. Observational studies
  3. Surveys
  4. Experiments
  5. Simulations
  6. Research ethics
  7. Mass collaboration
  8. Developing a research proposal

Course Schedule

Note: Schedule is subject to change. Check back here and on Canvas for updates as the course progresses.

Week

Day

Class

Readings

1— 

What is computational social science research?


9/26 - 9/29

1

Introduction to the the class and Computational Social Science
  • Bhattacherjee, A. (2012). Social science research: Principles, methods, and practices. (Chapters 1-4; Extremely useful for students without a strong background in the social sciences)
  • Chapter 2&4 - Doing honest work in college.
  • Bit by Bit - chapter 1

2

Introduction to Social Science Research Questions, Constructs, Operationalization
  • Watts, D. J. (2007). A twenty-first century science.
  • Lazer et. al. (2009) Computational Social Science.
  • Evans, J. (2020). Social computing unhinged.
  • Mental Exercise 1

2— Observational Studies


10/2 - 10/6

1

Counting Things
  • Chan, H. F., et al. (2021). Can psychological traits explain mobility behavior during the COVID-19 pandemic?
  • Bit by Bit - 2.1-2.4.1

2

Measurement
  • Shi, F., Shi, Y., Dokshin, F. A., Evans, J. A., & Macy, M. W. (2017). Millions of online book co-purchases reveal partisan differences in the consumption of science. Nature Human Behaviour, 1(4), 0079.
  • Stephens-Davidowitz, S. (2014). The cost of racial animus on a black candidate: Evidence using Google search data. Journal of Public Economics, 118, 26-40.
  • Mental Exercise 2

3— Observational Studies (continued)


10/9 - 10/13

1

Forecasting and Approximating Experiments
  • Bit by Bit – 2.4.3-2.5
  • Hersh, E. D. (2013). Long-term effect of September 11 on the political behavior of victims’ families and neighbors.

2

Approximating Experiments - Continued
  • Legewie, J. (2016). Racial profiling and use of force in police stops: How local events trigger periods of increased discrimination. American journal of sociology, 122(2), 379-424.
  • Sourati, J., & Evans, J. A. (2023). Accelerating science with human-aware artificial intelligence. Nature Human Behaviour, 1-15.

4— Asking Questions


10/16 - 10/20

1

Fundamentals
  • Bit by Bit – 3.1-3.7
  • (Optional) Wang, Wei, David Rothschild, Sharad Goel, and Andrew Gelman, "Forecasting Elections with Non-Representative Polls," International Journal of Forecasting, 31:3 (2015) pp. 980-991.
  • (Optional) Blumenstock, J., Cadamuro, G., & On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), 1073-1076.

2

Digitally-Enriched
  • York Cornwell, E., & Cagney, K. A. (2017). Aging in activity space: results from smartphone-based GPS-tracking of urban seniors. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 72(5), 864-875.
  • Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the national academy of sciences, 110(15), 5802-5805.
  • Mental Exercise 4

5— Experiments


10/23 -

10/27

1

Running Digital Experiments
  • Bit by Bit – chapter 4
  • (Optional) Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization.

2

Discussing Digital Experimental Design
  • Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762), 854-856.
  • Van de Rijt, A., Kang, S. M., Restivo, M., & Patil, A. (2014). Field experiments of success-breeds-success dynamics. Proceedings of the National Academy of Sciences, 111(19), 6934-6939.
  • Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Hunzaker, M. F., ... & Volfovsky, A. (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37), 9216-9221.

6— Ethics


10/30 -

11/3

1

Going through Salganik’s Four Principles
  • Bit by Bit – chapter 6

2

Discussing the Assigned Readings
  • Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks.
  • Watts, D. J. (2014). Stop complaining about the Facebook study. It's a golden age for research.
  • Zimmer, M. (2010). "But the data is already public": on the ethics of research in Facebook.
  • Mental Exercise 5

7— Simulation


11/6 -

11/10

1

Growing Artificial Societies

Read at least one of the following:

  • Page, S. (2015) “What sociologists should know about complexity.” Annual Review of Sociology 41: 21–41.
  • Epstein and Axtell (1996) Growing Artificial Societies: Social Science from the Bottom Up (p. 1-53)
  • Dean et al. (2006) "Understanding Anasazi Culture Change through Agent-based Modeling"

Exercise:

Cool stuff: 

2

“Explaining” Data via Simulation
  • DellaPosta et al. (2015) "Why Do Liberals Drink Lattes?" 
  • Goldberg and Stein (2018) "Beyond Social Contagion."
  • Mental Exercise 6
  • Optional exercise: If you download/install NetLogo, you can play around with some model parameters for yourself (Sugarscape and AA are two sample models included with the installation)

8— Mass Collaboration, Part 1 & Writing a Research proposal


11/13 -

11/17

1

Human Computation

 
  • Bit by Bit – chapter 5.1-5.2

2

 Generating a Research Question

11/20-11/24

Thanksgiving Break (No Class)

10 — Mass Collaboration, Part 2 & Research Design


11/27 - 12/1

1

From Research Question to Well-Researched Proposal
  • Bit by Bit – chapter 5.3-5.6
  • Optional: Bell, R. M., Koren, Y., & Volinsky, C. (2010). All together now: A perspective on the netflix prize.
  • Optional: Salganik, M. J., Lundberg, I., Kindel, A. T., Ahearn, C. E., Al-Ghoneim, K., Almaatouq, A., ... & McLanahan, S. (2020). Measuring the predictability of life outcomes with a scientific mass collaboration.
  • Optional: Salesses, P., Schechtner, K., & Hidalgo, C. A. (2013). The collaborative image of the city: mapping the inequality of urban perception.
  • Booth et al (2016). The Craft of Research. Chapters 3&4
  • What are Citation Management Tools?
  • Which Tool is Best for me?

2

Proposal Workshop