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In this lecture, Jockers will describe a method he's developed for detecting and measuring plot structure in long from fiction. He'll show how he uses this method, along side some basic machine clustering, to empirically identify six fundamental plot shapes within a collection of 45,000 novels. Jockers will conclude with a discussion of best sellers and the canon and show how the data from his model can be used to isolate the plot shapes that are most successful over time and across genre.

Matthew L. Jockers is Associate Professor of English at the University of Nebraska, Faculty Fellow in the Center for Digital Research in the Humanities, Faculty Fellow in the Center for Great Plains Studies, and Director of the Nebraska Literary Lab. His research is focused on computational approaches to the study of literature, especially large collections of literature. His books include Macroanalysis: Digital Methods and Literary History (University of Illinois, 2013) as well as Text Analysis Using R for Students of Literature (Springer, 2014). Jockers has written articles on computational text analysis, authorship attribution, Irish and Irish-American literature, and he has co-authored several important amicus briefs defending the fair and transformative use of digital text. Jockers's research has been profiled in the New York Times, Nature, the Chronicle of Higher Education, Nautilus, Wired, New Scientist, Smithsonian, NBC News and many others.