• Meeting day/time: MW 11:30a-1:20p, 247 Saieh Hall for Economics
• Lab session: W 4:30-5:20p, 247 Saieh Hall for Economics
• Office hours also available by appointment

• Meeting day/time: MW 11:30a-1:20p, 301 Rosenwald Hall
• Lab session: W 4:30-5:20p, 115 Cobb Hall
• Office hours also available by appointment

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 such as observational studies and experiments, statistical summaries, visualization of data, and how computational opportunities can enhance them. The focus of the course is on exploring the wide range of contemporary approaches to computational social science, with problem sets, programming exercises, and written assignments to gain experience with these methods.

• Introduce major research paradigms in computational social science
• Read and critique recent seminal papers
• Develop an original research topic
• Implement exploratory methods for analyzing data

All textbooks are available in electronic editions either directly from the author or via the UChicago library (authentication required). Hardcopies can be purchased at your preferred retailer.

• Booth, Wayne et al., The Craft of Research, 4th ed. University of Chicago Press, 2016.
• Salganik, Matthew J., Bit by Bit: Social Research in the Digital Age, Princeton University Press, 2018.

Grades will be based on your performance on eight assignments, each of which is worth 10 points with the exception of the research proposal. Some of these will be writing assignments. Some of these will be computational exercises.

• You must submit your assignments by creating a copy of the homework repository using the provided GitHub Classroom link. All contents must be committed and pushed to this repo by the assignment deadline.
• Assignments will be given on the day listed in the course schedule below. In general, assignments will be due before class at 11:30am a week after they are assigned. However, exact due dates and times will be listed on the assignment.

The TAs will hold a Wednesday night lab to discuss what constitutes plagiarism and how to avoid it. Academic honesty is an extremely important principle in academia and at the University of Chicago.

• Writing assignments must put in quotes and cite any excerpts taken from another work.
• If the cited work is the particular paper referenced in the Assignment, no works cited or references are necessary at the end of the composition.
• If the cited work is not the particular paper referenced in the Assignment, you MUST include a works cited or references section at the end of the composition.
• Any copying of other students' work will result in a zero grade and potential further academic discipline.

Unexcused late problem sets will be penalized 1 points for every hour they are late. For example, if an assignment is due on Monday at 11:30am, the following points will be deducted based on the time stamp of the last commit.

1. Introduction to computational social science
   • Watts, D. J. (2007). A twenty-first century science. Nature, 445(7127), 489-489.
   • Lazer et. al. (2009) Computational Social Science. Science, 323, 721-723.
   • "Chapter 1: Introduction." Bit by Bit.
2. Observational data (counting things)
   • "Chapter 2: Observing Behavior." Bit by Bit. Sections 2.1-2.4.1.3.
   • Edelman, B. G., & Luca, M. (2014). Digital discrimination: The case of airbnb.com. Harvard Business School NOM Unit Working Paper, (14-054).
   • King, G., Pan, J., & Roberts, M. E. (2013). How censorship in China allows government criticism but silences collective expression. American Political Science Review, 107(02), 326-343.
   • Kossinets, G., & Watts, D. J. (2006). Empirical analysis of an evolving social network. Science, 311(5757), 88-90.
3. Observational data (measurement)
   • Bonica, A. (2014). Mapping the ideological marketplace. American Journal of Political Science, 58(2), 367-386.
   • Wojcik, S. P., Hovasapian, A., Graham, J., Motyl, M., & Ditto, P. H. (2015). Conservatives report, but liberals display, greater happiness. Science, 347(6227), 1243-1246.
   • 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. -- available on course reserve on the Canvas site
4. Observational data (forecasting)
   • 2.4.2 Forecasting and nowcasting. Bit by Bit.
   • Goel, S., Hofman, J. M., Lahaie, S., Pennock, D. M., & Watts, D. J. (2010). Predicting consumer behavior with Web search. PNAS, 107(41), 17486-17490.
   • Beieler, J., Brandt, P. T., Halterman, A., Schrodt, P. A., Simpson, E. M., & Alvarez, R. M. (2016). Generating political event data in near real time. Computational Social Science, 98. -- available on course reserve on the Canvas site
   • Google Flu Trends
     • Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012-1014.
     • Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google flu: traps in big data analysis. Science, 343(6176), 1203-1205.
5. Observational data (approximating experiments)
   • 2.4.3 Approximating experiments. Bit by Bit.
   • Phan, T. Q., & Airoldi, E. M. (2015). A natural experiment of social network formation and dynamics. PNAS, 112(21), 6595-6600.
   • Hersh, E. D. (2013). Long-term effect of September 11 on the political behavior of victims' families and neighbors. PNAS, 110(52), 20959-20963.
   • Einav, L., Kuchler, T., Levin, J., & Sundaresan, N. (2015). Assessing sale strategies in online markets using matched listings. American Economic Journal: Microeconomics, 7(2), 215-47.
6. Asking questions (fundamentals)
   • "Chapter 3: Asking Questions." Bit by Bit. Sections 3.1-3.4.
   • Ansolabehere, S., & Hersh, E. (2012). Validation: What big data reveal about survey misreporting and the real electorate. Political Analysis, 20(4), 437-459.
   • Schuldt, J. P., Konrath, S. H., & Schwarz, N. (2011). "Global warming" or "climate change"? Whether the planet is warming depends on question wording. Public Opinion Quarterly, 75(1): 115-124.
   • Wang, W., Rothschild, D., Goel, S., & Gelman, A. (2015). Forecasting elections with non-representative polls. International Journal of Forecasting, 31(3), 980-991.
   • The Upshot: We Gave Four Good Pollsters the Same Raw Data. They Had Four Different Results.
7. Asking questions (digitally-enriched)
   • "Chapter 3: Asking Questions." Bit by Bit. Sections 3.5-3.7.
   • Sugie, N. F. (2016). Utilizing Smartphones to Study Disadvantaged and Hard-to-Reach Groups. Sociological Methods & Research, 0049124115626176.
   • Lax, J. R., & Phillips, J. H. (2009). How should we estimate public opinion in the states?. American Journal of Political Science, 53(1), 107-121.
   • 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.
8. Experiments
   • "Chapter 4: Running experiments." Bit by Bit.
   • 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. Nature, 489(7415), 295-298.
   • Edelman, B., Luca, M., & Svirsky, D. (2017). Racial discrimination in the sharing economy: Evidence from a field experiment. American Economic Journal: Applied Economics, 9(2), 1-22.
   • Schultz, P. W., Nolan, J. M., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V. (2007). The constructive, destructive, and reconstructive power of social norms. Psychological science, 18(5), 429-434.
9. Experiments (more)
   • Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Evaluating online labor markets for experimental research: Amazon. com's Mechanical Turk. Political Analysis, 20(3), 351-368.
   • King, G., Pan, J., & Roberts, M. E. (2014). Reverse-engineering censorship in China: Randomized experimentation and participant observation. Science, 345(6199), 1251722.
   • Munger, K. (2017). Tweetment effects on the tweeted: Experimentally reducing racist harassment. Political Behavior, 39(3), 629-649.
10. Collaboration
    • "Chapter 5: Collaborating". Bit by Bit.
    • Bell, R. M., Koren, Y., & Volinsky, C. (2010). All together now: A perspective on the netflix prize. Chance, 23(1), 24-29.
11. Collaboration (cont.)
    • "Chapter 5: Collaborating". Bit by Bit.
12. Ethics
    • "Chapter 6: Ethics." Bit by Bit.
    • Zimmer, M. (2016). OkCupid Study Reveals the Perils of Big-Data Science. Wired.
    • Burnett, S., & Feamster, N. (2015, August). Encore: Lightweight measurement of web censorship with cross-origin requests. In ACM SIGCOMM Computer Communication Review (Vol. 45, No. 4, pp. 653-667). ACM.
    • Facebook emotional contagion study
      • Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. PNAS, 111(24), 8788-8790.
      • Editorial Expression of Concern: Experimental evidence of massive-scale emotional contagion through social networks. (2014) PNAS, 111(29), 10779.
      • Watts, D. J. (2014). Stop complaining about the Facebook study. It's a golden age for research. The Guardian.
      • Zimmer, M. (2010). “But the data is already public”: on the ethics of research in Facebook. Ethics and information technology, 12(4), 313-325.
13. Ethics (cont.)
    • Eubanks, V. (2018). Chapter 4: The Allegheny algorithm. In Automating inequality: How high-tech tools profile, police, and punish the poor (pp. 127-173). St. Martin's Press. -- available on course reserve on the Canvas site
    • UChicago Social & Behavioral Sciences Institutional Review Board
      • Skim site
      • Specifically read "Does My Research Need IRB Review?"
14. Developing a research proposal
15. Developing a research proposal
16. Exploratory data analysis
17. Exploratory data analysis
18. Exploratory data analysis
19. Exploratory data analysis

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.

This course is open to all students who meet the academic requirements for participation. Any student who has a documented need for accommodation should contact Student Disability Services (773-702-6000 or [email protected]) and provide the instructor of their section (Dr. Soltoff or Dr. Clindaniel) with a copy of your Accommodation Determination Letter as soon as possible.