Topics in Computational Social Science

University of Texas at Austin, Department of Sociology

Thursday 3:30pm–6:30pm
Location: RLP 3.106

Course Instructor:
Casey Breen (Email: casey.breen@austin.utexas.edu)
🕒 Office Hours Sign-up (Tuesdays from 11:30am–2:00pm): Book Link

Overview

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

Week Date Format Topic Code
1 Thurs, January 15 Lecture Intro CSS Lecture
2 Thurs, January 22 Lab Lab 0 – Intro to R sessions PS1; PS2
3 Thurs, January 29 Lecture Social Data in the Digital Age
4 Thurs, February 5 Lab Lab 1 – Social Data Collection Lab 1
5 Thurs, February 12 Lecture Machine Learning + Prediction I Demo — DHS data
6 Thurs, February 19 Lecture Final Paper Topic Pitches Demo — Data Visualization
7 Thurs, February 26 Lecture Machine Learning + Prediction II Demo — OCR
8 Thurs, March 5 Lab Lab 2 – Machine Learning Lab 2
9 Thurs, March 12 Lecture Non-probability sampling Demo - Google Trends
10 Thurs, March 19 — Spring Break — No Class
11 Thurs, March 26 Lecture Social Networks
12 Thurs, April 2 Lab Lab 3 – Non-probability sampling Lab 3
13 Thurs, April 9 Lecture Agent Based Models
14 Thurs, April 16 Lecture Text as Data / Large Language Models
15 Thurs, April 23 Lecture Final Presentations

Weekly Schedule

We will discuss readings marked with a star (★) in class.

Thurs, January 15 — Introduction to Computational Social Science

  • 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.★
  • Salganik, Matthew J. 2019. Bit by Bit: Social Research in the Digital Age. Princeton University Press. Chapter 1.★
  • 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 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.

Open

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Thurs, January 22 — Intro to R Session [LAB]

Please bring your laptop to class with R and RStudio installed; also install the tidyverse package.

Helpful Resources:

  • R for Data Science Link
  • Data Visualization: A practical introduction Link

Thurs, Jan 29th — Social data in the digital age

  • 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.★
  • 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.★
  • 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.★
  • Breen, Casey F., and Dennis M. Feehan. 2024. “New Data Sources for Demographic Research.” Population and Development Review 51(1):539–73. doi:10.1111/padr.12671.★
  • Ruggles, Steven. 2014. ‘Big Microdata for Population Research’. Demography 51(1):287–97. doi: 10.1007/s13524-013-0240-2.
  • 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.
  • 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.

Open

Thurs, Feb 5 — Social data in the digital age [LAB]

Quarto Doc

Collect original data on the count of Facebook users broken down by characteristics using the Facebook marketing API

  • 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.

Thurs, Feb 12 — Machine Learning I

  • 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.★
  • 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.★

Open

Thurs, Feb 19 — Paper Topic Pitches

Students will give short presentations outlining their proposed final projects. Each pitch should clearly state the research question, explain its substantive importance, and describe the data and computational methods to be used.

The session will focus on constructive feedback (be kind!) to help refine questions, assess feasibility, and ensure projects are well scoped.

Thurs, Feb 26 — Machine Learning II

  • 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.★
  • Phillips, Rachael V., Mark J. van der Laan, Hana Lee, and Susan Gruber. 2023. “Practical Considerations for Specifying a Super Learner.” International Journal of Epidemiology 52(4):1276–85. doi:10.1093/ije/dyad023.

Open

Thurs, March 5 — Lab 2, Machine Learning

Quarto Doc

Apply machine learning algorithms and Facebook user counts to predict overall levels of internet adoption and digital divisions

  • 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’.★

Thurs, March 12 — Probability and 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.★
  • Schneider, Daniel, and Kristen Harknett. 2022. “What’s to Like? Facebook as a Tool for Survey Data Collection.” Sociological Methods & Research 51(1):108–40. doi:10.1177/0049124119882477.★
  • 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.

Open

Thurs, March 26 — 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 David 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.

Open

Thurs, April 2 — Lab 3

Quarto Doc

Apply poststratification and inverse-probability weighting to a non-probability sample to estimate mortality rates using network survival method

  • 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.

Thurs, April 9 — Agent Based Models

  • Bruch, Elizabeth, and Jon Atwell. “Agent-based models in empirical social research.” Sociological methods & research44, 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.★

Open

Thurs, April 16 — Text as Data / Large Language Models in Social Science Research

  • Grimmer, Justin, and Brandon M. Stewart. 2013. “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts.” Political Analysis 21(3):267–97. doi:10.1093/pan/mps028.★
  • Waight, Hannah, Yin Yuan, Margaret E. Roberts, and Brandon M. Stewart. 2025. “The Decade-Long Growth of Government-Authored News Media in China under Xi Jinping.” Proceedings of the National Academy of Sciences 122(11):e2408260122. doi:10.1073/pnas.2408260122.★
  • Hastings, Orestes P. and Luca Maria Pesando. 2024. “What’s a Parent to do? Measuring Cultural Logics of Parenting with Computational Text Analysis.” Social Science Research 124:103074.
  • Bail Christopher A. 2024. “Can Generative AI Improve Social Science?” Proceedings of the National Academy of Sciences. 121(21):e2314021121.

Thurs, April 23 — Final Presentations

Students will deliver 10-minute presentations of their final projects. Presentations should clearly communicate the research question, data, methods, and main findings, with special attention to the computational approach applied. Each presentation will be followed by brief Q&A.

Learning Outcomes

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

  • Critically evaluate and engage with contemporary scientific debates within the area of computational social science and identify key contributions
  • Apply computational social science perspectives to formulate and address relevant sociological and demographic questions
  • 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
  • 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 four computer labs. 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. 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.

Acknowledgements

The first iteration of this course was originally co-developed and co-taught with the wonderful Ridhi Kashyap at the University of Oxford. Many elements of this course build directly on that collaboration.