Note: slides will be added as the course progresses.

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

LINK TO SLIDES (.pdf)

Background reading

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

LINK TO SLIDES (.pdf)

Background reading

Readings for discussion

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

LINK TO SLIDES (.pdf)

Background reading

Readings for discussion

Topics: Introduction to automated text analysis: key concepts; selecting documents and features. Sources of textual data. String manipulation in R. Regular expressions. Text processing with quanteda.

LINK TO SLIDES (.pdf)

Background reading

No readings for discussion this week

Topics: Describing and comparing textual data.

LINK TO SLIDES (.pdf)

Background reading

Readings for discussion

Topics: Dictionary methods.

LINK TO SLIDES (.pdf)

Background reading

Readings for discussion

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

LINK TO SLIDES (.pdf)

Background reading

Readings for discussion

Topics: Unsupervised machine learning (topic models).

LINK TO SLIDES (.pdf)

Background reading

Readings for discussion

Topics: Word embeddings.

LINK TO SLIDES (.pdf)

Background reading

Readings for discussion

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

LINK TO SLIDES (.pdf)

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

LINK TO SLIDES (.pdf)

Background reading

Readings for discussion

Topics: Dimensionality reduction. Latent space network models.

LINK TO SLIDES (.pdf)

Readings for discussion

Topics: Parallel programming with R. Introduction to SQL.

LINK TO SLIDES (.pdf)

Background reading

No class: Thanksgiving Holiday.

Topics: Parallel programming with R. Good coding practices

LINK TO SLIDES (.pdf)

Job market and industry careers advice.