The slides for my presentation at the 21st St. Louis Symposium on German Literature at Washington University: Distant Readings / Descriptive Turns: Topologies of German Culture in the Long Nineteenth Century are available here.

For those interested in learning more, I want to point to the following resources. These vary in their assumptions of background knowledge; I’ve tried to put the more introductory material first within each section.

#### Topic models

(NB: This is a partial selection. I’ve tried to focus on material of interest to those working in the human and social sciences)

- Blei, David. Introduction to probabilistic topic models
- Mimno, David. “Computational Historiography in a Century of Classics Journals”
- Hall, et al. “Studying the History of Ideas Using Topic Models.” (Hall, Jurafsky, and Manning 2008)
- Hall, David. “Studying the History of Ideas Using Topic Models.” MS Thesis. (Hall 2008)
- Chang et al. (2009) “Reading Tea Leaves: How Humans Interpret Topic Models”
- Wallach, Hanna, et al. (2009) "Rethinking LDA: Why Priors Matter."
- Wallach, Hanna (2008) "Structured Topic Models for Language."
- Political Science: Justin Grimmer, Arthur Spirling’s US Treaties paper
- Block and Newman (2011) “What, Where, When, and Sometimes Why: Data Mining Two Decades of Women’s History Abstracts”

#### Software implementing LDA

- topicmodels (R package)
- MALLET (Java software)

#### Text Analysis

- Unix for Poets
- Lecture 1 and Lecture 2 from Cosma Shalizi’s data mining course.
- Introduction to Information Retrieval (Manning and Schütze) (open-access)

#### Probability

- Hacking, Ian.
*An introduction to probability and inductive logic* - Grinstead and Snell. Introduction to probability (open-access)

#### LDA

- Edwin Chen’s Introduction to Latent Dirichlet Allocation
- Gregor Heinrich (2004) “Parameter estimation for text analysis.”
- Griffiths (2004) “Finding scientific topics.”

#### Bayesian Statistics

- Peter Hoff (2009)
*A First Course in Bayesian Statistical Methods* - Kadane. Principles of Uncertainty (open-access)

#### Machine Learning

#### References

Block, Sharon, and David Newman. 2011. “What, Where, When, and Sometimes
Why: Data Mining Two Decades of Women’s History Abstracts.” *Journal of
Women’s History* 23: 81–109.
http://muse.jhu.edu/journals/journal_of_womens_history/v023/23.1.block.html.

Chang, J., J. Boyd-Graber, S. Gerrish, C. Wang, and D. M. Blei. 2009. “Reading Tea Leaves: How Humans Interpret Topic Models.” http://umiacs.umd.edu/%7Ejbg/docs/nips2009-rtl.pdf.

Griffiths, T. L. 2004. “Finding scientific topics.” *Proceedings of the
National Academy of Sciences* 101 (jan): 5228–5235.
doi:10.1073/pnas.0307752101.
http://www.pnas.org/content/101/suppl.1/5228.abstract.

Hall, David. 2008. “Studying the History of Ideas Using Topic Models.” Stanford University. http://symsys.stanford.edu/theses/thesis1.pdf.

Hall, David, Daniel Jurafsky, and Christopher D. Manning. 2008.
“Studying the History of Ideas Using Topic Models.” In *Proceedings of
the Conference on Empirical Methods in Natural Language Processing*,
363–371. Honolulu, Hawaii: Association for Computational Linguistics.

Heinrich, Gregor. 2004. “Parameter estimation for text analysis.” http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.1327.

Hoff, Peter D. 2009. *A First Course in Bayesian Statistical Methods*.
Springer.