Matt Jockers Lecture:

Matt Jockers Lecture: "Linguistic Entailments, Bestselling DNA, and other Absurd Ideas”

In this talk, Jockers describes how algorithms reveal the unique patterns of individual linguistic style and allow us to predict which authors and which books are mostly likely to hit the New York Times Bestseller list. He discusses foundational work in authorship attribution, stylometry—and even some neuroscience and behavioral genetics—in a talk that ultimately leads us to question the entire notion of creativity and authorial agency.

Matthew Jockers is a distinguished research scientist and senior engineering manager at Apple where he leads the research teams that help customers discover great books, movies, and podcasts.

Prior to joining Apple, Jockers served as Dean of the College of Arts and Sciences and Professor of Literature and Data Analytics at Washington State University. As an academic administrator, Jockers was viewed as a bridge builder committed to a highly interdisciplinarity vision of higher education. He believes that to advance knowledge universities must aggressively pursue the truth and deliver a diverse range of coursework that encourages students to cross boundaries and span divides. In industry and in academia, Jockers has been committed to the idea that excellence and innovation are dependent upon three core pillars: strong culture, shared mission, and diversity of ideas.

Jockers was born in suburban New York but celebrated his first birthday on a ranch in Livingston, Montana where his grandfather raised cattle. Throughout his youth he moved back and forth between Montana and New York eventually settling in Montana at age 17. He spent his teenage years wrangling horses and guiding wilderness pack trips in Wyoming and Montana. As an undergraduate at Montana State University, he studied Literature and history while working a range of jobs that included washing dishes, tending bar, logging firewood, pounding nails, and selling fly-fishing gear in sporting goods shop. After college he worked as a dry-wall contractor before earning an assistantship to pursue a PhD.

As a professor and scholar, Jockers was a key contributor to a second wave of computationally driven literary criticism that his colleague Franco Moretti termed “distant reading.” Together they founded and built the Stanford Literary Lab, and in 2010, the Chronicle of Higher Education wrote that Jockers and Moretti were the “Lewis and Clark of the literary frontier.” Jockers’s research draws from expertise in the humanities and the social and data sciences. As an academic, he deployed natural language processing and machine learning to extract and understand cultural trends and linguistic patterns in large collections of literature. He authored numerous research articles in stylometry and authorship attribution as well as a handful of books that include Macroanalysis: Digital Methods and Literary History (2013), Text Analysis with R for Students of Literature (two editions 2014, 2020), and The Bestseller Code: Anatomy of the Block Buster Novel (2016). The algorithms at the heart of his research on The Bestseller Code won the University of Nebraska’s Breakthrough Innovation of the Year award in 2018 (https://youtu.be/dWbVsWnQz1g)

Alongside his academic career, Jockers has worked in industry and in a non-profit. He was founding Director of the digital 501(c)(3) Western Institute of Irish Studies and Director of Research at Novel Projects (a data-driven book recommendations startup that was acquired in 2014). In 2017 he co-founded Archer Jockers, LLC, a book industry consulting company that he ran for two years with his Bestseller Code co-author, and former Stanford student, Jodie Archer. In 2019 he cofounded Authors A.I. with JD Lasica and Alessandra Torre. Authors A.I. is a text mining company that leverages artificial intelligence and data analytics to help writers develop and market successful novels.

Jockers has been married for 30 years. He has two children and two dogs. Outside of work, Jockers is most often found waist deep in a river casting after steelhead and trout with a twohanded fly rod.

More info:

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Matt's Personal Website