I published Redlining Culture: A Data History of Racial Inequality and Postwar Fiction with Columbia University Press in 2020. The book uses data driven methods, such as statistical models and text-mining, to write a new history of the post-war American novel, exploring dynamics of race and publishing, book reviews, sales, and prizewinners, as well as the university. I argue that we should understand the post-war period as the era that created a model of white supremacy in culture - one that persists today - rather than an era of "multiculturalism," as usually claimed. During this period, major publishers are 97% white in terms of authors; reviews are 90% white; prizewinners are 91% white; and bestsellers are 98% white - and these numbers are unchanging in this period. I use novel methods in machine learning to explore at scale how this inequality has impacted the form and content of the novels themselves: the creation of a persistent "white voice" in American literature, at the expense of racial minority authors. I conclude by asking: how did we get here, why haven't we recognized this inequality as foundational to post-war American fiction, and how can we fix the problem today. I wrote an op-ed based on the book for The New York Times and The Nation interviewed me about it.
Right now I'm working on a new project: The Fast Revolution: Race, Writing, and Protest after the Social Web. It explores how over the past decade (2013 to 2020), the rise of online writing platforms, such as Twitter and Wattpad, reciprocally converged with the rise of the #BlackLivesMatter movement to transform how both institutions and ordinary people talk about race. In particular, I'm interested in how this convergence sparked new narratives about race, transforming our standard models of racial representation rooted in print culture, from newspapers to novels. In method, I combine data analysis (e.g., text analytics of 10 million tweets), critical race theory, and close reading. Broadly, the project tries to make sense of the growing impact of the Internet and user generated content (UGC) on democracy and protest in terms of how we've come to tell stories about democracy and protest.
And as always, I'm working on a bunch of collaborative, one-off pieces.
- "How a Pandemic Becomes a Story, 2020," with Hoyt Long (Chicago) and Kaitlyn Todd (McGill) - computational and critical analysis of large corpora of Wattpad user generated content "COVID" stories to discern how regular people have internalized and turned COVID into a narrative, and further, how this ad hoc storytelling might anticipate more formal works of culture like film and television.
- "Fictionality and Fandom After the Social Web," with Aarthi Vadde (Duke) - data driven study of the fan fiction website AO3 to examine new forms of fictionality in the Internet age, particularly as induced by social community reader dynamics. We focus on the relationship between gender and character space to discover a series of otherwise unnoticed narrative constraints regarding "shipping."
- "Twitter's Intimate Publics: #BLM, Affect, Narrative," with Long Le-Khac (Loyola) and Maria Antoniak (Cornell/Twitter) - computational study of the emergence of the "racial awakening" narrative on Twitter in the summer of 2020 amongst white liberals, focused on modeling personal stories of ideological conversion and their impact on public discourse, particularly the mainstream media, like the NYT.
- "Narrative Theory after the Computational Turn," building on work I've been doing with David Bamman (Berkeley) and Andrew Piper (McGill), this is a method piece exploring the affordances of new machine learning tools for narrative analysis, arguing that NLP and narratology have a lot to teach each other.