How do W.E.B. du Bois’s data portraits communicate racial politics? How do Henry David Thoreau’s writings document climate change? Why are Samuel Beckett's characters often labeled autistic? And why does Hsia Yu use machines to translate found poetry? In our contemporary culture, where data not only informs but often dictates discourse, these questions underscore the profound intertwining of data with literary, historical, and sociopolitical narratives. In the past decades, historians use network analysis to discover previously unknown historical connections, literary scholars utilize sentiment analysis, natural language processing, and text mining to identify genres and other stylistic patterns, and media studies scholars use web scraping to develop digital archives. While data science has proven its analytical prowess, its potential for storytelling is often overlooked in many data-driven projects. This course aims to examine the theoretical foundation of data-driven storytelling and explore how data is incorporated into contemporary transmedia storytelling.
The course structure revolves around four main modules—language, agent, world, and representation—which form the crucial elements of storytelling. Each week, we delve into a key concept under each category, while also developing computational tools to probe its potential for creative storytelling. This is a beginner-friendly course tailored for individuals without any coding experience, serving as an introduction to Python programming. Weekly materials are presented in a Jupyter notebook, incorporating tutorials and exercises. At times, these include step-by-step tutorials, designed to be easily followed, customized, and integrated into their own projects by students. At other times, they only introduce some existing solutions and projects. Upon concluding each module, students will also engage with an extended case drawn from specific contexts within literary and cultural history. Utilizing the tools and methodologies acquired during the course and beyond, students will be encouraged to devise "solutions" to these open-ended questions.
- Jonathan Culler, “The Linguistic Foundation,” Structuralist Poetics
- Jonathan Culler, “Story and Discourse in Analysis of Narrative”
- Monika Fludernik, “Narrative and Narrating”
- Andrew Piper, “Toward a Data-Driven Theory of Narrativity”
- Hugh Kenner, “Beckett Thinking,” The Mechanic Muse
- Hannes Bajohr, “Algorithmic Empathy: Toward a Critique of Aesthetic AI”
- Hoyt Long and Richard Jean So, “Literary Pattern Recognition”
- Nick Montfort, Taroko Gorge (2009)
- Nick Montfort et al., Taroko Gorge Remix (2016)
- Katherine Bode, “Literary Studies in the Digital Age,” Reading by Numbers
- Andrew Piper, “There Will Be Numbers”
- Andrew Piper, “Punctuation (Opposition),” Enumerations
- Andrew Goldstone and Ted Underwood, “The Quiet Transformation of Literary Studies”
- Jonathan Basile, The Library of Babel (2015)
- Andrew Piper, “Think Small: On Literary Modeling”
- Katherine Bode, “Why You Can’t Model Away Bias”
- Richard Jean So, “All Models Are Wrong”
- Nan Z. Da, “The Computational Case against Computational Literary Studies”
- Brian Hayes, “First Links in the Markov Chain”
- Hsia Yu, Pink Noise (2007)
- Ethem Alpaydin, “Introduction,” Machine Learning
- Peter Norvig and Stuart J. Russell, “Introduction,” Artificial Intelligence: A Modern Approach
- Katherine Hayles, “Nonconscious Cognitions, Humans and Others,” Unthought
- Marco Bernini, “Modeling the Apparent Self,” Beckett and the Cognitive Method
- Nick Montfort, Megawatt (2014)
- Rosalind W. Picard, "Introduction," Affective Computing
- Katherine Elkins, “The Birth of a Field,” The Shapes of Stories
- Simone Rebora, “Sentiment Analysis in Literary Studies: A Critical Survey”
- Michal Kosinski, “Theory of Mind May Have Spontaneously Emerged in Large Language Models”
- Stephen Haney et al., DearDiary.ai (2022)
- Ted Underwood and Richard Jean So “Can We Map Culture?”
- Wendy Hui Kyong Chun, “Queerying Homophily,” Pattern Discrimination
- Wendy Hui Kyong Chun, “How to Destroy the World, One Solution at A Time,” Discriminating Data
- Ted Underwood, “Why Literary Time Is Measured in Minutes”
- Richard B. Primack et al., “Was Henry David Thoreau a Good Naturalist?”
- Richard B. Primack, Walden Warming: Climate Change Comes to Thoreau's Woods
- David Bering-Porter, “Data as Symbolic Form”
- Orit Halpern, “Visualizing,” Beautiful Data
- W. E. B. Du Bois's Data Portraits (2018)
- Mitchell Akiyama, “Dataffect: Numerical Epistemology and the Art of Data Sonification”
- David Cecchetto, “Incommunication,” Listening in the Afterlife of Data
- Bill Manaris, “Sonification and Big Data,” JythonMusic
- Brian Foo, Two Trains (2015)
- Brian Foo, Still I Rise: From Spoken Word To Sheet Music (2016)