Computational Social Science Methods (PA397C|INF385T)

Course description

This research-oriented course introduces and contextualizes computational methods from a social science research design perspective. The course has three major components:

  1. Readings and presentations on theoretical analysis. We will mainly focus on how to comprehend computational methods from a research design perspective, i.e., data management, concept representation, data analysis, and scientific communication.
  2. Practice and presentations on hands-on exercises. We have a range of bi-weekly assignments helping you experiment or practice a variety of computational methods.
  3. Reflections on methods and empirical studies.

I strongly encourage you to responsibly use ChatGPT or similar generative AI tools to boost your productivity. Bilingual or multilingual language ability is a plus. Programming is an essential part of this course but not the purpose and will not be taught. We will be coding for social science purposes.

Reading materials / e-books

Required readings:

This class will use a draft book manuscript I’m currently working on:

Additionally, each week is complemented with readings from various other sources. See details on Schedule page.

Recommended readings:

These books give you a good theoretical understanding and are very useful in research design.

  • [GRS] Grimmer, Justin, Margaret E. Roberts, and Brandon M. Stewart. 2022. Text as Data: A New Framework for Machine Learning and the Social Sciences. Princeton, New Jersey Oxford: Princeton University Press.
  • [SJ] Scott, John. 2017. Social Network Analysis. Fourth edition. Thousand Oaks, CA: SAGE Publications Ltd. (different versions are fine)

These books/sources introduce more technical and hands-on details.

  • [GS] 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.
  • [JM] Jurafsky, Daniel, and James H. Martin. 2022. Speech and Language Processing. 3rd draft. https://web.stanford.edu/~jurafsky/slp3/. (the authors generously made their book publicly available, check their website and use the latest version)
  • NetworkX (the package’s documentation and the references cited are the best place to start in terms of technical details)

Resources

Submissions from previous semesters

Computing resources

Students of this class have access to below high-performance computing resources:

Grading

Assignments (TBD)

  • A >= 95%, A- >= 90
  • B+ >= 87%, B >= 83%, B- >= 80%
  • C+ >= 77%, C >= 73%, C- >= 70%
  • D+ >= 67%, D >= 63%, D- >= 60%

Policies

  • Mental health: The instructor urge students who are struggling for any reason and who believe that it might impact their performance in the course to reach out if they feel comfortable. This will allow the instructor to provide any possible resources or accommodations. If immediate mental health assistance is needed, call the Counseling and Mental Health Center (CMHC) at 512-471-3515. You may also contact Bryce Moffett, LCSW (LBJ CARE counselor) at 512-232-4449 or stop by her office hours-Wednesday 1-2 pm SRH 3.119. Outside CMHC business hours (8a.m.-5p.m., Monday-Friday), contact the CMHC 24/7 Crisis Line at 512-471-2255.
  • University Policies
  • By taking this course (either for credit or auditing), you automatically authorize the instructor to use or cite the contents created by you for this course in the instructor’s working book project. Appropriate academic principles of attribution and integrity will be followed.
  • License for Open Education: This syllabus and all course content on this public website created by the instructor, TA, and students are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.
  • License of assignments: For the assignment examples submitted by creators to OSF, the creators of submissions are the owners of their submissions. The owners grant CC BY 4.0 DEED to their submissions.

Acknowledgements


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