Computational Social Science Methods for Policy and Nonprofit Studies (PA397C|INF385T)

Course Schedule

Reading Materials by Week

Knowledge and hands-on fundamentals

Text analysis in social sciences

Network analysis in social sciences

Week 0 Pre-course Back2Top

Week 1 Sep 9: Why this course? Back2Top

Before class

  • Readings:
    • Lazer, David, Alex Pentland, Lada Adamic, Sinan Aral, Albert-László Barabási, Devon Brewer, Nicholas Christakis, et al. 2009. “Computational Social Science.” Science 323 (5915): 721–23. https://doi.org/10.1126/science.1167742.
    • Cioffi-Revilla, Claudio. 2017. “Introduction.” In Introduction to Computational Social Science, 1–33. Texts in Computer Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-50131-4.

In class

  • Discussion and lecture on readings.
  • Course review: Syllabus, assignments, final project.

After class

Week 2 Sep 16: Knowledge graph, computation, and social science Back2Top

Before class

  • Readings:
    • Miller, Eric J. 2001. “An Introduction to the Resource Description Framework.” Journal of Library Administration 34 (3–4): 245–55. https://doi.org/10.1300/J111v34n03_04.
    • Cioffi-Revilla, Claudio. 2017. “Computation and Social Science.” In Introduction to Computational Social Science. Texts in Computer Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-50131-4.
  • Software packages:
    • Yu, Shih Yuan, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, and Mohammad Abdullah Al Faruque. 2019. “Pykg2vec: A Python Library for Knowledge Graph Embedding.” ArXiv:1906.04239 [Cs, Stat], June. http://arxiv.org/abs/1906.04239.
    • Costabello, Luca, Sumit Pai, Chan Le Van, Rory McGrath, and Nick McCarthy. 2019. “AmpliGraph: A Library for Representation Learning on Knowledge Graphs,” March. https://doi.org/10.5281/zenodo.2595043.

In class

  • Discussion and lecture on readings.
  • Group discussion: How can you use knowledge graph and RDF to solve a social science problem?
  • Hands-on:

After class

Week 3 Sep 23: Good enough research practices in scientific computing Back2Top

Before class

  • Readings:
    • Gentzkow, Matthew, and Jesse M. Shapiro. 2014. Code and Data for the Social Sciences: A Practitioner’s Guide.
    • 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.
    • Wilson, Greg, Jennifer Bryan, Karen Cranston, Justin Kitzes, Lex Nederbragt, and Tracy K. Teal. 2017. “Good Enough Practices in Scientific Computing.” PLOS Computational Biology 13 (6): e1005510. https://doi.org/10.1371/journal.pcbi.1005510.

In class

  • Discussion and lecture on readings.
  • Hands-on:
    • Let’s use JupyterHub Server on Chameleon Cloud.
    • Install htop (not easy): 
        $ sudo apt-get update
  $ sudo apt-get install build-essential
  $ sudo apt-get install libncurses5-dev libncursesw5-dev
  $ wget https://hisham.hm/htop/releases/2.2.0/htop-2.2.0.tar.gz
  $ tar xvfvz htop-2.2.0.tar.gz
  $ cd htop-2.2.0
  $ ./configure; make; sudo make install
    • Define a function then parallel a job.

After class

Week 4 Sep 30: High-performance cloud computing and parallel computing Back2Top

Before class

  • Readings:
    • Czech, Zbigniew J. 2017. “Concurrent Processes.” In Introduction to Parallel Computing, 1–34. Cambridge University Press.

In class

After class

Week 5 Oct 7: Algorithm and workflow for social scientists Back2Top

Before class

  • Readings:
    • Bird, Steven, Ewan Klein, and Edward Loper. 2009. “Writing Structured Programs.” In Natural Language Processing with Python. Beijing ; Cambridge [Mass.]: O’Reilly.
    • Cioffi-Revilla, Claudio. 2017a. “Automated Information Extraction.” In Introduction to Computational Social Science. Texts in Computer Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-50131-4.

In class:

After class

Week 6 Oct 14: Text analysis - Fundamentals and lexical resources Back2Top

Before class

  • Complete all lessons on RegexOne
  • Readings:
    • Jurafsky, Daniel, and James H. Martin. 2017. “Regular Expressions, Text Normalization, Edit Distance.” In Speech and Language Processing, 3rd draft. https://web.stanford.edu/~jurafsky/slp3/.
    • Bird, Steven, Ewan Klein, and Edward Loper. 2009a. “Accessing Text Corpora and Lexical Resources.” In Natural Language Processing with Python. Beijing ; Cambridge [Mass.]: O’Reilly.

In class

After class

Week 7 Oct 21: Text analysis - Preprocessing and vectorization Back2Top

Before class

  • Readings:
    • Bengfort, Benjamin, Rebecca Bilbro, and Tony Ojeda. 2018a. “Corpus Preprocessing and Wrangling.” In Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning, 1 edition. Beijing Boston Farnham Sebastopol Tokyo: O’Reilly Media.
    • ———. 2018b. “Text Vectorization and Transformation Pipelines.” In Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning, 1 edition. Beijing Boston Farnham Sebastopol Tokyo: O’Reilly Media.

In class

After class

  • Natural Language Processing Fundamentals in Python:
    • “Building a ‘fake news’ classifier”
  • Further readings:
    • Bird, Steven, Ewan Klein, and Edward Loper. 2009a. “Categorizing and Tagging Words.” In Natural Language Processing with Python. Beijing ; Cambridge [Mass.]: O’Reilly.
    • ———. 2009b. “Preprocessing Raw Text.” In Natural Language Processing with Python. Beijing ; Cambridge [Mass.]: O’Reilly.

Week 8 Oct 28: Text analysis - Classification Back2Top

Before class

  • Readings:
    • Bengfort, Benjamin, Rebecca Bilbro, and Tony Ojeda. 2018. “Classification for Text Analysis.” In Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning, 1 edition. Beijing Boston Farnham Sebastopol Tokyo: O’Reilly Media.
    • Bird, Steven, Ewan Klein, and Edward Loper. 2009. “Learning to Classify Text.” In Natural Language Processing with Python. Beijing ; Cambridge [Mass.]: O’Reilly.

In class

After class

Week 9 Nov 4: Text analysis - Relation extraction Back2Top

Before class

  • Readings:
    • Bengfort, Benjamin, Rebecca Bilbro, and Tony Ojeda. 2018. “Graph Analysis of Text.” In Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning, 1 edition. Beijing Boston Farnham Sebastopol Tokyo: O’Reilly Media.

In class

After class

Week 10 Nov 11: Text analysis - Application in social science studies Back2Top

Before class

  • Readings:
    • 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.
    • Anastasopoulos, L. Jason, and Andrew B. Whitford. 2019. “Machine Learning for Public Administration Research, With Application to Organizational Reputation.” Journal of Public Administration Research and Theory 29 (3): 491–510. https://doi.org/10.1093/jopart/muy060.

In class

After class

Week 11 Nov 18: Network analysis - basic concepts Back2Top

Before class

  • 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.
    • Scott, John. 2017. “Organising and Analysing Network Data.” In Social Network Analysis, Fourth edition. Thousand Oaks, CA: SAGE Publications Ltd.

In class

After class

  • Further readings:
    • Scott, John. 2017. “Data Collection for Social Network Analysis.” In Social Network Analysis, Fourth edition. Thousand Oaks, CA: SAGE Publications Ltd.
    • Scott, John. 2017. “The History of Social Network Analysis.” In Social Network Analysis, Fourth edition. Thousand Oaks, CA: SAGE Publications Ltd.
  • Network Analysis in Python (Part 1)
    • “Introduction to networks”

Week 12 Nov 25: Thanksgiving week, no class Back2Top

Week 13 Dec 2: Network analysis - analysis of nodes and communities Back2Top

Before class

  • Readings:
    • Scott, John. 2017. “Popularity Mediation and Exclusion.” In Social Network Analysis, Fourth edition. Thousand Oaks, CA: SAGE Publications Ltd.
    • Colizza, V., A. Flammini, M. A. Serrano, and A. Vespignani. 2006. “Detecting Rich-Club Ordering in Complex Networks.” Nature Physics 2 (2): 110–15. https://doi.org/10.1038/nphys209.

In class

After class

Week 14 Dec 9: Network analysis - Example social science studies Back2Top

Before class

  • Readings:
    • 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

After class


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