Editors’ Choice: What can topic models of PMLA teach us about the history of literary scholarship?

Of all our literary-historical narratives it is the history of criticism itself that seems most wedded to a stodgy history-of-ideas approach—narrating change through a succession of stars or contending schools. While scholars like John Guillory and Gerald Graff have produced subtler models of disciplinary history, we could still do more to complicate the narratives that organize our discipline’s understanding of itself.


A browsable network based on Underwood's model of PMLA. Click through, then mouse over or click on individual topics.

A browsable network based on Underwood’s model of PMLA.Click through, then mouse over or click on individual topics.

The archive of scholarship is also, unlike many twentieth-century archives, digitized and available for “distant reading.” Much of what we need is available through JSTOR’s Data for Research API. So last summer it occurred to a group of us that topic modeling PMLA might provide a new perspective on the history of literary studies. Although Goldstone and Underwood are writing this post, the impetus for the project also came from Natalia Cecire, Brian Croxall, and Roger Whitson, who may do deeper dives into specific aspects of this archive in the near future.

Topic modeling is a technique that automatically identifies groups of words that tend to occur together in a large collection of documents. It was developed about a decade ago by David Bleiamong others. Underwood has a blog postexplaining topic modeling, and you can find a practical introduction to the technique at the Programming Historian. Jonathan Goodwinhas explained how it can be applied to the word-frequency data you get from JSTOR.

Obviously, PMLA is not an adequate synecdoche for literary studies. But, as a generalist journal with a long history, it makes a useful test case to assess the value of topic modeling for a history of the discipline.

Goldstone and Underwood each independently produced several different models ofPMLA, using different software, stopword lists, and numbers of topics. Our results overlapped in places and diverged in places. But we’ve reached a shared sense that topic modeling can enrich the history of literary scholarship by revealing trends that are presently invisible.


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This content was selected for Digital Humanities Now by Editor-in-Chief based on nominations by Editors-at-Large