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

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/.
  • Prepare your computational environment, make sure that your Jupyter Lab server has these packages installed:
    • NLTK: Preprocessing.
    • Stanza: Preprocessing, POS, NER, sentiment analysis.
    • Gensim: Preprocessing, vectorization, topic modeling (fixed word-embedding).
    • BERTopic: Topic modeling (fixed and contextualized word-embedding, multilingual support, visualization).
    • Top2Vec: Topic modeling (fixed and contextualized word-embedding, multilingual support). I recently used it for a multilingual topic modeling task.
    • SentenceTransformers: Vectorize sentences or documents. Used by many proceeding packages. I sometime use it to obtain the raw vector values if analysis requires (e.g., calculating text similarity in this and this article, visualizing semantic spaces, etc.)
    • Transformers: Train or fine-tune pretrained BERT models. Used by many proceeding packages. I used it to fine-tune a BERT model for classifying nonprofits according to their mission statements.

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

Before class

  • Recommended readings:
    • Gerring, John. 2012. “Mere Description.” British Journal of Political Science 42 (4): 721–46. https://doi.org/10.1017/S0007123412000130.
    • Grimmer, J., &Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3), 267–297. doi:10.1093/pan/mps028.
  • 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 10: Research design: Data analysis (student-lead) (TBD) Back2Top

Before class

  • Recommended readings:
  • 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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