Topics in Computational Social Science

Tuesdays 16:00-18:00pm, Sociology Seminar Room

[Jointly developed and co-instructed with Ridhi Kashyap]

The growing availability of new streams of data, expansion of computational power and the digitalisation of our lives has created new questions and research opportunities for social and population scientists. The course will introduce students to a range of methodological and substantive topics in computational social science. We will cover topics such as digital trace and big data, machine learning, non-probability sampling, social networks, and agent-based modelling and microsimulation. The course will consist of seminar and lab sessions (taught in R), where students will engage with research in computational social science and learn to apply basic computational methods to research problems based on existing research papers.

Course Schedule

Learning Outcomes:

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

1. Critically evaluate and engage with contemporary scientific debates within the area of computational social science and identify key contributions;

2. Apply computational social science perspectives to formulate and address relevant sociological and demographic questions;

3. Demonstrate proficiency in some techniques of computational social science, including methods for processing and analysing digital and unstructured data, basic methods for machine learning and addressing bias in non-probability samples;

4. Apply and engage with a computational social science perspective to address their own research problems and areas of interests.

Teaching arrangement:

The teaching will be organised in a combination of lectures, discussion-based seminars, and three computer labs. All weeks will feature an introductory lecture, followed by either a discussion seminar (weeks 1, 3, 5, 7, and 8) or lab (weeks 2, 4, 6).

In discussion seminar weeks (1, 3, 5, 7 and 8), readings marked with * in the reading list will serve as core readings for that week. We are asking all participants to present one of these core readings in a max 10 minute presentation (no slides required) during the course in the format of a synthesis and peer review. The presenter for the readings for a given week will be made available on Canvas the previous week.

In presenting the paper to the group, please focus on: What is the objective of the paper, what are the main contributions, and what are its limitations? If it is an empirical paper, does the analysis/data seem convincing and does the strength of the argument match the data and evidence? If it is a theoretical/perspective paper, what questions and ideas does it generate for research in computational social science?

All participants – and not only those presenting – are expected to read, at minimum, all core papers for the week. We strongly encourage you to review all readings for a given week.

Requirements:

The course will be taught in R (using RStudio). Students are expected to have R and RStudio downloaded and be familiar with R. We recommend completing a basic introductory course in R via CodeAcademy or equivalent prior to the course. As an additional resource, we suggest the following text, which is freely available online:

• Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund. 2023. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (2nd Edition). O’Reilly.

Course Assessment:

The course will be examined by:

1. Essay (max 3000 words) in response to one from a list of questions provided by instructors, provided in week 8, due on Friday, noon, week 0 Trinity term;

2. Problem set, involving code and markdown report, that applies computational techniques learned during the module, provided in week 8, due on Friday, noon, week 0 Trinity term.

Reading list

Note: starred (★) papers are for presentation to the group by the assigned student for that week. These are the core readings for the week.

Week 1: Introduction to Computational Social Science

• Kashyap, Ridhi, R. Gordon Rinderknecht, Aliakbar Akbaritabar, Diego Alburez-Gutierrez, Sofia Gil-Clavel, André Grow, Jisu Kim et al. “Digital and computational demography.” In Research Handbook on Digital Sociology, pp. 48-86. Edward Elgar Publishing, 2023.★
• Lazer, David, Eszter Hargittai, Deen Freelon, Sandra Gonzalez-Bailon, Kevin Munger, Katherine Ognyanova, and Jason Radford. 2021. ‘Meaningful Measures of Human Society in the Twenty-First Century’. Nature 595(7866):189–96. doi: 10.1038/s41586-021-03660-7.★
• Edelmann, Achim, Tom Wolff, Danielle Montagne, and Christopher A. Bail. “Computational social science and sociology.” Annual review of sociology 46, no. 1 (2020): 61-81.★
• Lazer, David M. J., Alex Pentland, Duncan J. Watts, Sinan Aral, Susan Athey, Noshir Contractor, Deen Freelon, Sandra Gonzalez-Bailon, Gary King, Helen Margetts, Alondra Nelson, Matthew J. Salganik, Markus Strohmaier, Alessandro Vespignani, and Claudia Wagner. 2020. ‘Computational Social Science: Obstacles and Opportunities’. Science 369(6507):1060–62. doi: 10.1126/science.aaz8170.*
• Salganik, Matthew J. 2019. Bit by Bit: Social Research in the Digital Age. Princeton University Press. Chapter 1.★

Week 2: Social data in the digital age - opportunities and challenges

• Ruggles, Steven. 2014. ‘Big Microdata for Population Research’. Demography 51(1):287–97. doi: 10.1007/s13524-013-0240-2.
• Kashyap, Ridhi. 2021. ‘Has Demography Witnessed a Data Revolution? Promises and Pitfalls of a Changing Data Ecosystem’. Population Studies 75(sup1):47–75. doi: 10.1080/00324728.2021.1969031.★
• Salganik, Matthew J. 2019. Bit by Bit: Social Research in the Digital Age. Princeton University Press. Chapter 2.★
• Lazer, David, Ryan Kennedy, Gary King, and Alessandro Vespignani. 2014. ‘The Parable of Google Flu: Traps in Big Data Analysis’. Science 343(6176):1203–5. doi: 10.1126/science.1248506.
• Ruggles, Steven, Catherine A. Fitch, and Evan Roberts. 2018. ‘Historical Census Record Linkage’. Annual Review of Sociology 44(1):19–37. doi: 10.1146/annurev-soc-073117-041447.★
• Chetty, Raj, Matthew O. Jackson, Theresa Kuchler, Johannes Stroebel, Nathaniel Hendren, Robert B. Fluegge, Sara Gong, Federico Gonzalez, Armelle Grondin, Matthew Jacob, Drew Johnston, Martin Koenen, Eduardo Laguna-Muggenburg, Florian
• Mudekereza, Tom Rutter, Nicolaj Thor, Wilbur Townsend, Ruby Zhang, Mike Bailey, Pablo Barberá, Monica Bhole, and Nils Wernerfelt. 2022. ‘Social Capital I: Measurement and Associations with Economic Mobility’. Nature 608(7921):108–21. doi: 10.1038/s41586-022-04996-4.
• Bruch, Elizabeth E., and M. E. J. Newman. 2018. ‘Aspirational Pursuit of Mates in Online Dating Markets’. Science Advances 4(8):eaap9815. doi: 10.1126/sciadv.aap9815.★
• Grinberg, Nir, Kenneth Joseph, Lisa Friedland, Briony Swire-Thompson, and David Lazer. 2019. ‘Fake News on Twitter during the 2016 U.S. Presidential Election’. Science 363(6425):374–78. doi: 10.1126/science.aau2706.

Week 2: Collecting Facebook data using an Application Program Interface (API) [LAB 1]

• Fatehkia, Masoomali, Ridhi Kashyap, and Ingmar Weber. “Using Facebook ad data to track the global digital gender gap.” World Development 107 (2018): 189-209.★
• Zagheni, Emilio, Ingmar Weber, and Krishna Gummadi. 2017. ‘Leveraging Facebook’s Advertising Platform to Monitor Stocks of Migrants’. Population and Development Review 43(4):721–34.
• Araujo, Matheus, Yelena Mejova, Ingmar Weber, and Fabricio Benevenuto. “Using Facebook ads audiences for global lifestyle disease surveillance: Promises and limitations.” In Proceedings of the 2017 ACM on Web science conference, pp. 253-257. 2017.
• Collect original data on the count of Facebook users broken down by characteristics using the Facebook marketing API

Week 3: Machine learning/prediction

• Salganik, Matthew J., Ian Lundberg, …, and Sara McLanahan. 2020. ‘Measuring the Predictability of Life Outcomes with a Scientific Mass Collaboration’. Proceedings of the National Academy of Sciences 117(15):8398–8403. doi: 10.1073/pnas.1915006117.★
• Lundberg, Ian, Jennie E. Brand, and Nanum Jeon. 2022. ‘Researcher Reasoning Meets Computational Capacity: Machine Learning for Social Science’. Social Science Research 108:102807. doi: 10.1016/j.ssresearch.2022.102807.
• Lundberg, Ian, Rachel Brown-Weinstock, Susan Clampet-Lundquist, Sarah Pachman, Timothy J. Nelson, Vicki Yang, Kathryn Edin, and Matthew J. Salganik. 2024. ‘The Origins of Unpredictability in Life Outcome Prediction Tasks’. Proceedings of the National Academy of Sciences 121(24):e2322973121. doi: 10.1073/pnas.2322973121.
• Blumenstock, Joshua, Gabriel Cadamuro, and Robert On. 2015. ‘Predicting Poverty and Wealth from Mobile Phone Metadata’. Science 350(6264):1073–76. doi: 10.1126/science.aac4420.★
• Chi, Guanghua, Han Fang, Sourav Chatterjee, and Joshua E. Blumenstock. 2022. ‘Microestimates of Wealth for All Low- and Middle-Income Countries’. Proceedings of the National Academy of Sciences 119(3):e2113658119. doi: 10.1073/pnas.2113658119.★
• Molina, Mario, and Filiz Garip. 2019. ‘Machine Learning for Sociology’. Annual Review of Sociology 45(1):27–45. doi: 10.1146/annurev-soc-073117-041106.
• Hofman, Jake M., Duncan J. Watts, Susan Athey, Filiz Garip, Thomas L. Griffiths, Jon Kleinberg, Helen Margetts, Sendhil Mullainathan, Matthew J. Salganik, Simine Vazire, Alessandro Vespignani, and Tal Yarkoni. 2021. ‘Integrating Explanation and Prediction in Computational Social Science’. Nature 595(7866):181–88. doi: 10.1038/s41586-021s-03659-0.

Week 4: Machine learning [LAB 2]

• Breen, Casey F., Masoomali Fatehkia, Jiani Yan, Xinyi Zhao, Douglas R. Leasure, Ingmar Weber, and Ridhi Kashyap. 2024. ‘Mapping Subnational Gender Gaps in Internet and Mobile Adoption Using Social Media Data’.

Week 5: Non-probability sampling

• Stedman, Richard C., Nancy A. Connelly, Thomas A. Heberlein, Daniel J. Decker, and Shorna B. Allred. 2019. ‘The End of the (Research) World As We Know It?★
• Understanding and Coping With Declining Response Rates to Mail Surveys’. Society & Natural Resources 32(10):1139–54. doi: 10.1080/08941920.2019.1587127.
• Lehdonvirta, Vili, Atte Oksanen, Pekka Räsänen, and Grant Blank. 2021. ‘Social Media, Web, and Panel Surveys: Using Non-Probability Samples in Social and Policy Research’. Policy & Internet 13(1):134–55. doi: 10.1002/poi3.238.★
• Elliott, Michael R., and Richard Valliant. 2017. ‘Inference for Nonprobability Samples’. Statistical Science 32(2):249–64.
• Wang, Wei, David Rothschild, Sharad Goel, and Andrew Gelman. 2015. ‘Forecasting Elections with Non-Representative Polls’. International Journal of Forecasting 31(3):980–91. doi: 10.1016/j.ijforecast.2014.06.001.★
• Dutwin, David, and Trent D. Buskirk. 2017. ‘Apples to Oranges or Gala versus Golden Delicious?: Comparing Data Quality of Nonprobability Internet Samples to Low Response Rate Probability Samples’. Public Opinion Quarterly 81(S1):213–39. doi: 10.1093/poq/nfw061.
• Park, David K., Andrew Gelman, and Joseph Bafumi. 2004. ‘Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls’. Political Analysis 12(4):375–85. doi: 10.1093/pan/mph024.
• Breen, Casey F., Ayesha S. Mahmud, and Dennis M. Feehan. 2022. ‘Novel Estimates Reveal Subnational Heterogeneities in Disease-Relevant Contact Patterns in the United States’. PLOS Computational Biology 18(12):e1010742. doi: 10.1371/journal.pcbi.1010742.

Week 6: Social networks

• Borgatti, Stephen P., Ajay Mehra, Daniel J. Brass, and Giuseppe Labianca. 2009. ‘Network Analysis in the Social Sciences’. Science 323(5916):892–95. doi: 10.1126/science.1165821.
• Watts, Duncan J. 2004. ‘The “New” Science of Networks’. Annual Review of Sociology 30(1):243–70. doi: 10.1146/annurev.soc.30.020404.104342.★
• Eagle, Nathan, Alex (Sandy) Pentland, and DavThe “New” Science of Networks | Annual Reviewsid Lazer. 2009. ‘Inferring Friendship Network Structure by Using Mobile Phone Data’. Proceedings of the National Academy of Sciences 106(36):15274–78. doi: 10.1073/pnas.0900282106.
• Kossinets, Gueorgi, and Duncan J. Watts. 2006. ‘Empirical Analysis of an Evolving Social Network’. Science 311(5757):88–90. doi: 10.1126/science.1116869.
• Feehan, Dennis M., and Curtiss Cobb. 2019. ‘Using an Online Sample to Estimate the Size of an Offline Population’. Demography 56(6):2377–92. doi: 10.1007/s13524-019-00840-z.
• Granovetter, Mark S. 1973. ‘The Strength of Weak Ties’. American Journal of Sociology 78(6):1360–80.★
• Salganik, Matthew J., and Douglas D. Heckathorn. 2004. ‘Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling. Sociological Methodology 34(1):193–240.★
• Goel, Sharad, and Matthew J. Salganik. 2010. ‘Assessing Respondent-Driven Sampling’. Proceedings of the National Academy of Sciences 107(15):6743–47. doi: 10.1073/pnas.1000261107.
• Centola, Damon, and Michael Macy. 2007. ‘Complex Contagions and the Weakness of Long Ties’. American Journal of Sociology 113(3):702–34. doi: 10.1086/521848.*

Week 7: Network Survival Method + non-probability Sampling [LAB 3]

• Baker, Reg, J. Michael Brick, Nancy A. Bates, Mike Battaglia, Mick P. Couper, Jill A. Dever, Krista J. Gile, and Roger Tourangeau. 2013. ‘Summary Report of the AAPOR Task Force on Non-Probability Sampling’. Journal of Survey Statistics and Methodology 1(2):90–143. doi: 10.1093/jssam/smt008.
• Breen, Casey F., Saeed Rahman, Christina Kay, Joeri Smits, Abraham Azar, Steve Ahuka-Mundeke, and Dennis M. Feehan. 2025. ‘Estimating Death Rates in Complex Humanitarian Emergencies Using the Network Survival Method’.

Week 8: Agent-based modelling and microsimulation

• Bruch, Elizabeth, and Jon Atwell. “Agent-based models in empirical social research.” Sociological methods & research. 44, no. 2 (2015): 186-221.★
• Spielauer, Martin. “What is social science microsimulation?” Social Science Computer Review 29, no. 1 (2011): 9-20.
• Zagheni, Emilio. “The impact of the HIV/AIDS epidemic on kinship resources for orphans in Zimbabwe.” Population and Development Review. 37, no. 4 (2011): 761-783.
• Kashyap, Ridhi, and Francisco Villavicencio. “The dynamics of son preference, technology diffusion, and fertility decline underlying distorted sex ratios at birth: A simulation approach.” Demography. 53, no. 5 (2016): 1261-1281.★
• Grow, André, and Jan Van Bavel. “Assortative mating and the reversal of gender inequality in education in Europe: An agent-based model.” PloS one 10, no. 6 (2015): e0127806.
• Alburez‐Gutierrez, Diego, Carl Mason, and Emilio Zagheni. “The “sandwich generation” revisited: Global demographic drivers of care time demands.” Population and Development Review. 47, no. 4 (2021): 997-1023.★