Computational Social Science Methods (PA397C|INF385T)

Course Schedule

Reading Materials by Week

Computational methods and research design fundamentals (4 weeks)

Analyzing computational social science methods (8 + 1 weeks)

Instructor-lead sessions are voted by the class before 1/27 from these options

Final project

  • Week 14: Final project presentations

Weekly Details

Week 1: Course introduction Back2Top

Before class

  • Readings:
    • Hofman, Jake M., Duncan J. Watts, Susan Athey, Filiz Garip, Thomas L. Griffiths, Jon Kleinberg, Helen Margetts, et al. 2021. “Integrating Explanation and Prediction in Computational Social Science.” Nature 595 (7866): 181–88. https://doi.org/10.1038/s41586-021-03659-0.
    • Edelmann, Achim, Tom Wolff, Danielle Montagne, and Christopher A. Bail. 2020. “Computational Social Science and Sociology.” Annual Review of Sociology 46 (1): 61–81. https://doi.org/10.1146/annurev-soc-121919-054621.
    • Lazer, David M. J., Alex Pentland, Duncan J. Watts, Sinan Aral, Susan Athey, Noshir Contractor, Deen Freelon, et al. 2020. “Computational Social Science: Obstacles and Opportunities.” Science 369 (6507): 1060–62. https://doi.org/10.1126/science.aaz8170.

In class

  • Course overview:
    • Motivation and history of this course.
    • Course sites: Syllabus website, Canvas, and how to use them.
    • Helpful resources: open source communities, ChatGPT (and how to responsibly use it for educational purposes), etc.
  • Review final project options.
  • Discussion on readings: Analytical capacity of CSS methods
  • Review CSS Empirical Studies Database.

After class

Week 2: Computational methods for social sciences: Overview Back2Top

Before class

  • Ragin, Charles C., and Lisa M. Amoroso. 2011. “The Goals of Social Research.” In Constructing Social Research: The Unity and Diversity of Method, 135–62. Pine Forge Press.
  • Leonelli, Sabina. 2020. “Scientific Research and Big Data.” In The Stanford Encyclopedia of Philosophy, edited by Edward N. Zalta, Summer 2020. Metaphysics Research Lab, Stanford University. https://plato.stanford.edu/archives/sum2020/entries/science-big-data/.

In class

Video recording

  • How to use Chameleon Cloud: Set up a new instance
  • How to set up a Jupyter Lab server

After class

Week 3: Analyzing computational methods from a research design perspective Back2Top

Before class

  • Ragin, Charles C., and Lisa M. Amoroso. 2011. “What Is (and Is Not) Social Research?” In Constructing Social Research: The Unity and Diversity of Method, 5–32. Pine Forge Press.
  • Ma, Ji, Islam Akef Ebeid, Arjen de Wit, Meiying Xu, Yongzheng Yang, René Bekkers, and Pamala Wiepking. 2021. “Computational Social Science for Nonprofit Studies: Developing a Toolbox and Knowledge Base for the Field.” VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, October. https://doi.org/10.1007/s11266-021-00414-x.

In class

  • Discussion and lecture on readings.
  • Discussion on final project options.

Week 4: Field visit: Texas Advanced Computing Center (TBD) Back2Top

In class

  • Visit TACC.
  • Discussion on final project options.

After class

Week 5: Computational methods: NLP algorithms and models as concept representation tools Back2Top

Before class

  • Required readings (copies of GRS chapters are on course’s Canvas site because of copyright)
    • Grimmer, Justin, Margaret E. Roberts, and Brandon M. Stewart. 2022. “Social Science Research and Text Analysis.” In Text as Data: A New Framework for Machine Learning and the Social Sciences. Princeton, New Jersey Oxford: Princeton University Press.
    • Grimmer, Justin, Margaret E. Roberts, and Brandon M. Stewart. 2022. “Principles of Measurement.” In Text as Data: A New Framework for Machine Learning and the Social Sciences. Princeton, New Jersey Oxford: Princeton University Press.
    • Jurafsky, Daniel, and James H. Martin. 2022. “Vector Semantics and Embeddings.” In Speech and Language Processing, 3rd draft. https://web.stanford.edu/~jurafsky/slp3/.
  • Recommended readings:
    • Rodriguez, Pedro L., and Arthur Spirling. 2022. “Word Embeddings: What Works, What Doesn’t, and How to Tell the Difference for Applied Research.” The Journal of Politics 84 (1): 101–15. https://doi.org/10.1086/715162.
    • 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. https://doi.org/10.1093/pan/mps028.

In class

  • Overview: typical application of NLP in social science research
  • Hands-on:
    • Preprocess text with Stanza.
    • Vectorize words with pretrained models.
      • Calculate word similarity. Example studies:
        • Kozlowski, Austin C., Matt Taddy, and James A. Evans. 2019. “The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings.” American Sociological Review 84 (5): 905–49. https://doi.org/10.1177/0003122419877135.
        • Jones, Jason J., Mohammad Ruhul Amin, Jessica Kim, and Steven Skiena. 2020. “Stereotypical Gender Associations in Language Have Decreased Over Time.” Sociological Science 7 (January): 1–35. https://doi.org/10.15195/v7.a1.
      • Calculate document similarity with Word Mover Distance. Example studies:
        • Ma, Ji. 2022. “How Does an Authoritarian State Co-Opt Its Social Scientists Studying Civil Society?” VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, July. https://doi.org/10.1007/s11266-022-00510-6.
    • Vectorize documents/paragraphs/sentences with pretrained models.
      • Calculate document similarity between documents/paragraphs/sentences. Example studies:
        • Ma, Ji, and René Bekkers. 2023. “Consensus Formation in Nonprofit and Philanthropic Studies: Networks, Reputation, and Gender.” Nonprofit and Voluntary Sector Quarterly, January, 08997640221146948. https://doi.org/10.1177/08997640221146948.
      • Max length of input documents (caveat 1, caveat 2)

Video recording

After class

Practice the coding sessions, play with your own datasets, revise research proposal.

Week 6: Research design: Data management (student-lead) Back2Top

Before class

  • Recommended readings:
    • Baker, M. (2016). 1,500 scientists lift the lid on reproducibility. Nature News, 533(7604), 452. https://doi.org/10.1038/533452a
    • Wilson, Greg, D. A. Aruliah, C. Titus Brown, Neil P. Chue Hong, Matt Davis, Richard T. Guy, Steven H. D. Haddock, et al. 2014. “Best Practices for Scientific Computing.” PLOS Biology 12 (1): e1001745. https://doi.org/10.1371/journal.pbio.1001745.
    • Gentzkow, Matthew, and Jesse M. Shapiro. 2014. Code and Data for the Social Sciences: A Practitioner’s Guide. https://web.stanford.edu/~gentzkow/research/CodeAndData.pdf.
    • Wickham, Hadley. 2014. “Tidy Data.” The Journal of Statistical Software 59 (10). http://www.jstatsoft.org/v59/i10/.
    • Boyd, Nora Mills. 2018. “Evidence Enriched.” Philosophy of Science 85 (3): 403–21. https://doi.org/10.1086/697747.
    • Leonelli, Sabina. 2020. “Scientific Research and Big Data.” In The Stanford Encyclopedia of Philosophy, edited by Edward N. Zalta, Summer 2020. Metaphysics Research Lab, Stanford University. https://plato.stanford.edu/archives/sum2020/entries/science-big-data/.
  • Empirical readings (TBD by student group)

In class

  • Discussion and lecture on readings.
  • Discussion on final project options.

After class

Provide feedback to group report.

Week 7: Computational methods: topic modeling and classification (instructor-lead) Back2Top

Before class

No readings before class, practice the coding sessions from previous weeks, and prepare a sample dataset of text for your proposed research (we will need it in class for practice purposes).

In class

  • Overview: Technical background of topic modeling and classification, application in research
  • Hands-on:
    • Topic modeling based on different vectorization methods:
      • Static word embedding (universal-sentence-encoder-multilingual)
      • Contextual word embedding (BERT)
    • Generation of topic keywords
    • Classification of texts (code review)
    • Practice with your own datasets

After class

Practice the coding sessions, play with your own datasets, revise research proposal.

Week 8: Research design: Concept representation (student-lead) Back2Top

In class

  • Discussion and lecture on readings.
  • Discussion on final project options.

After class

Provide feedback to group report.

Week 9: Computational methods: Network analysis as a representation and analysis method (instructor-lead) Back2Top

Before class

  • Recommended readings:
    • Scott, John. 2017. “What Is Social Network Analysis?” In Social Network Analysis, Fourth edition. Thousand Oaks, CA: SAGE Publications Ltd.
    • Scott, John. 2017. “Terminology for Network Analysis.” In Social Network Analysis, Fourth edition, 73–94. Thousand Oaks, CA: SAGE Publications Ltd.
    • Watts, Duncan J. 2004. “The ‘New’ Science of Networks.” Annual Review of Sociology 30 (1): 243–70. https://doi.org/10.1146/annurev.soc.30.020404.104342.

In class

  • Discussion and lecture on readings.
  • Discussion on final project options.

After class

Provide feedback to group report.

Week 10: Research design: Data analysis (student-lead) (TBD) Back2Top

Before class

  • Recommended readings:
    • Hofman, Jake M., Duncan J. Watts, Susan Athey, Filiz Garip, Thomas L. Griffiths, Jon Kleinberg, Helen Margetts, et al. 2021. “Integrating Explanation and Prediction in Computational Social Science.” Nature 595 (7866): 181–88. https://doi.org/10.1038/s41586-021-03659-0.
    • Gerring, J. (2012). Mere Description. British Journal of Political Science, 42(4), 721–746. https://doi.org/10.1017/S0007123412000130
    • Humphreys, P. (2009). The philosophical novelty of computer simulation methods. Synthese, 169(3), 615–626. https://doi.org/10.1007/s11229-008-9435-2
  • Empirical readings (TBD by student group)

In class

  • Discussion and lecture on readings.
  • Discussion on final project options.

After class

Provide feedback to group report.

Week 13: Research design: Scientific communication (student-lead) (TBD) Back2Top

Before class

  • Recommended readings:
    • Kirk, Andy. 2019. Data Visualisation: A Handbook for Data Driven Design. 2nd edition. S.l.: SAGE Publications Ltd.
  • Empirical readings (TBD by student group)

In class

  • Discussion and lecture on readings.
  • Discussion on final project options.

After class

Provide feedback to group report.


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