Note: slides will be added as the course progresses.

Topics: Introductions and course overview. What is Computational Social Science? Good computing practices. Introduction to version control and GitHub

SLIDES (.pdf): Introduction to the course.

Background reading

Topics: Computational social science: research opportunities and challenges. Ethics of social science research in the digital age. Big data, big bias?

SLIDES (.pdf): Computational Social Science - Research opportunities and challenges.

Background reading

Readings for discussion

Topics: Large-scale online experiments. Data collection: automated data collection from the web.

SLIDES (.pdf): Experimental research in the digital age.

Background reading

Readings for discussion

Topics: Recent advances in survey research. Multilevel regression and poststratification. Parallel programming with R.

SLIDES (.pdf): Introduction to multilevel models in R.

Background reading

Readings for discussion

Topics: Introduction to automated text analysis. Sources of textual data. String manipulation in R. Regular expressions. Text processing with quanteda.

SLIDES (.pdf): Introduction to automated text analysis. Text preprocessing.

Background reading

No readings for discussion this week

Topics: Dictionary methods applied to sentiment analysis.

SLIDES (.pdf): Dictionary methods.

Background reading

Readings for discussion

Topics: Supervised machine learning applied to text classification. Crowd-sourcing the creation of training datasets.

SLIDES (.pdf): Introduction to supervised learning applied to text classification.

Background reading

Readings for discussion

Topics: Unsupervised machine learning (topic models).

SLIDES (.pdf): Introduction to unsupervised machine learning. Topic models. Describing and comparing documents.

Background reading

Readings for discussion

Topics: Introduction to word embeddings. Ideological scaling of documents. Event detection.

SLIDES (.pdf): Word embeddings.

Background reading

Readings for discussion

Topics: Basic concepts in social network analysis. Network visualization.

SLIDES (.pdf): Introduction to social network analysis.

Background reading

  • Sinclair, B. (2016). Network Structure and Social Outcomes: Network Analysis for Social Science, in Alvarez, M. (ed.) Computational Social Science. Cambridge: Cambridge University Press. [Full text available through USC libraries]

  • Chapters 1, 2, and 3 in Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets. Cambridge University Press.

Readings for discussion

Topics: Querying large-scale online datasets with SQL and Google BigQuery.

SLIDES (.pdf): Introduction to SQL.

Background reading

Topics: Social contagion processes: homophily vs influence. Collecting social media data.

SLIDES (.pdf): Social contagion processes. Twitter Data.

Background reading

Readings for discussion

Topics: Dimensionality reduction. Latent variable models.

SLIDES (.pdf): Principal component analysis. Latent space network models.

Readings for discussion

No class: Thanksgiving Holiday.

Student presentations of research projects. Course recap.

Other topics not covered in this year’s version of the course:

Audiovisual data (Images, audio, and video as data.)

Background reading

  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

Readings for discussion

Spatial data. Geographical Information Systems (GIS). Spatial autocorrelation. Spatial autoregresion models.

Background reading

Readings for discussion