Introduction to the course

Readings

• Cleanth Brooks, “The Heresy of Paraphrase” (1947) 
• Jean-Baptiste Michel, Erez Lieberman Aiden, et al., “Quantitative Analysis of Culture Using Millions of Digitized Books” (2011)  [paywalled; UCSB students have free access through the campus network or from off-campus through the UCSB VPN or Library Proxy server]

Readings

• Franco Moretti, Graphs, Maps, Trees (2007), pp. 1-33 (available on course Canvas)

Readings

• William Warner, Kimberly Knight, and UCSB Transliteracies History of Reading Group, “In the Beginning was the Word: A Visualization of the Page as Interface” (Note: the original Flash animation, which was interactive, is no longer viewable because the Flash program reached “end of life” at the end  of 2020 and is no longer supported in current browsers and operating systems. View instead the MP4 video version, or download the AVI or WMV video versions.)
• Yin Liu, “Ways of Reading, Models for Text, and the Usefulness of Dead People” [PDF or HTML] (2013)

Readings

• Alan Liu, “Transcendental Data: Toward a Cultural History and Aesthetics of the New Encoded Discourse” (2004) (read only pp. 49-57)

Readings

• Ryan Heuser and Long Le-Khac, “A Quantitative Literary History of 2,958 Nineteenth-Century British Novels: The Semantic Cohort Method” (2012) 

Readings

• Richard Jean So and Edwin Roland, “Race and Distant Reading” (2020)  (alternative URL)

Readings

• David M. Blei, “Probabilistic Topic Models” (2013)  — (read only to end of p. 79, before the math begins)
• Ted Underwood, “Topic Modeling Made Just Simple Enough” (2012)

Readings

• Andrew Goldstone and Ted Underwood, “The Quiet Transformations of Literary Studies: What Thirteen Thousand Scholars Could Tell Us” (2014)
• Andrew Goldstone, Topic model of 100 years of literary criticism journals (visualized in Goldstone’s Dfr-browser interface)

Readings

• Andrew Goldstone et al., Topic model of 40 years of the Signs journal of “Women in Culture and Society” (visualized in Goldstone’s Dfr-browser interface)
  • Helpful in learning how to work with Dfr-browser is the guide page on “Interpreting the topic model of Signs“
• Andrew Piper, excerpt from “Topoi (Dispersion),” in Enumerations: Data and Literary Study (2019)  — read only pp. 66–75 [available on course Canvas site]

Readings

• Background Readings in Linguistics Theory
  • Ferdinand de Saussure, Course in General Linguistics (1916)  – read pp. 114-117, 123-27.
  • J. [John] R. [Rupert] Firth, “A Synopsis of Linguistic Theory, 1930-55” (Oxford: Blackwell, 1957) – read sections III-IV (pp. 7-13) [available on course Canvas site]

Readings

• Luis Serrano, “What Are Word and Sentence Embeddings?” (2023)
  • Optional: create an account on the Cohere site mentioned in the article and try the “embedding” function in the Cohere “playground.” (See an example of results.)
• Saptarashmi Bandyopadhyay et al., “Word Embedding Demo: Tutorial” (2022) — Note: The actual interactive demo accompanying this tutorial about word embeddings (or word vectors) is assigned for the second part of Practicum 5.
• Nika Mavrody, Laura B. McGrath, Nichole Nomura, and Alexander Sherman, “Voice” (2021) — Read the “Abstract” and pp. 155-164.

Readings

• Ryan Heuser, “Word Vectors in the Eighteenth Century” (conference proceedings abstract) (2017)
  • Optional: If you are interested, you may wish to read Ryan Heuser’s series of blog posts about word embedding linked from this page on his blog (with individual posts on “Concepts,” “Methods,” “From Fields to Vectors,” and “Semantic Networks”).

Readings

• Grant Sanderson, Deep Learning/Neural Network Video Series (2017-2024). View in sequence the following four videos. (Other videos in the series are available in this YouTube playlist. You can also read the series in transcribed text form with illustrations here.)
  • “But What Is a Neural Network?”, YouTube video, running time 18:39
  • “Gradient Descent: How Neural Networks Learn,” YouTube video, running time 20:33
  • “Backpropagation, Step-by-Step,” YouTube video, running time 12:46
  • “Transformers (How LLMs Work) Explained Visually ,” YouTube video, running time 27:13
• Minh Hua and Rita Raley, “Playing With Unicorns: AI Dungeon and Citizen NLP” (2020) [If you can’t finish this article by this class, try to read at least the first two sections; and then finish the rest of the article for the next class.]

Optional (see the following if you are interested in learning more about this topic):

• Cornellius Yudha Wijaya, “Large Language Models Explained in 3 Levels of Difficulty” (2024)
• For a slightly deeper, more detailed explanation of neural networks as part of an explanation of large language models, see Andreas Stöffelbauer,  “How Large Language Models Work: From Zero to ChatGPT” (2023).

Readings

• Emily M. Bender, Timnit Gebru et al., “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” (2021)
  • [Optional: If you are interested in the controversy and background behind this article, see Tim Simonite, “What Really Happened When Google Ousted Timnit Gebru” (2021)]
• Alan Liu, “What Is Good Writing in the Age of ChatGPT?” (Commencement Address for the UCSB English Dept.,  2023).

Readings

• Stephen P. Borgatti, et al. (2009), “Network Analysis in the Social Sciences”
   alternative source: pre-copyedited manuscript of the article
• Elijah Meeks and Scott B. Weingart, “Introduction to Network Analysis and Representation” — click on the tabs for “centrality, ” “clustering coefficient,” etc. for brief interactive tutorials

Readings

• Paola Pascual-Ferrá, Neil Alperstein, and Daniel J. Barnett, “Social Network Analysis of COVID-19 Public Discourse on Twitter: Implications for Risk Communication” (2020)
• Richard Jean So and Hoyt Long, “Network Analysis and the Sociology of Modernism” (2013) — read only pp. 147-149, 158-166

Readings

• Christopher N. Warren et al., Six Degrees of Francis Bacon (2015)
  • Optional: for a detailed explanation of methods used to create this project, see Christopher N. Warren et al., “Six Degrees of Francis Bacon: A Statistical Method for Reconstructing Large Historical Social Networks” (2016)
• Franco Moretti, “Network Theory, Plot Analysis” (2011)  (Note: Because this essay in PDF format places its many social network graphs all at the end, a convenient way to read is to create and open two copies of the same essay, keeping one open to the graphs.)

Readings

• Student Presentations (presentations in class of final project proposals) (Note: the actual final project proposals must be submitted on the course Canvas site here by the end of June 11.)