Melanie Walsh

The Seattle Public Library (SPL) publishes anonymized, open-access checkout data for every item in its collection, dating from 2005 to the present. To our knowledge, it is the only U.S. library to release checkout data by title with this level of temporal detail: one dataset records exact timestamps for print book checkouts, while another provides monthly aggregates across all formats (e.g., ebooks, audiobooks, print books). Because U.S. book sales data is largely inaccessible outside the publishing industry, SPL’s open checkout data offers a rare and valuable alternative. But how well does it generalize beyond Seattle? Does it reflect book sales? And what can it tell us about readers more broadly? In this paper, we introduce SPL’s checkout data and evaluate its potential for humanistic and literary research. We specifically assess how well it: (1) corresponds with book sales, (2) and extrapolates to library checkout patterns elsewhere nationally. First, we compare SPL data against publishing revenue reports and prior research with access to sales figures. We find that SPL patrons embrace digital books more than general consumers, but the overall distribution of checkouts resembles broader book sales patterns. Second, we compare SPL’s most checked out books per year to the New York Public Library’s annual top 10 lists. We find general overlap, but also distinct regional preferences, suggesting geographic extrapolation should be approached with caution. We conclude that SPL’s checkout data provides a rare window into library circulation and reading habits, with granular, time-series insights. However, its generalizability—particularly across regions and in relation to book sales—remains uncertain.

In this data essay, we explore animal gender by analyzing 300 children’s books surveying 1,300+ people. Why are frogs always boys? Are ladybugs "ladies" in all languages?

Modernist poet T.S. Eliot concluded his 1925 poem "The Hollow Men" with the iconic lines: "This is the way the world ends / Not with a bang but a whimper." When Eliot died in 1965, the New York Times claimed in his obituary that these lines were “probably the most quoted lines of any 20th-century poet writing in English.” They may be among the most memed lines, as well. Through a computational analysis of Twitter data, we have found that at least 350,000 tweets have referenced or remixed Eliot’s lines since the beginning of Twitter’s history in 2006. While references to the poem vary widely, we focus on two prominent political usages of the phrase — cases where Twitter users invoke it to warn about the state of modern democracy, often from the left side of the political spectrum, and cases where they use the phrase to critique political correctness and “cancel culture” or to mock people for non-normatized aspects of their identities, often from the right side of the political spectrum. Though some of the tweets cite Eliot directly, most do not, and in many cases the phrase almost seems to be moving from an authored quotation into a common idiom or turn-of-phrase. Linguistics experts increasingly refer to this kind of construction as a “snowclone” —a fixed phrasal template, often with a culturally salient source (e.g., a quotation from a book, TV show, or movie), that has “one or more variable slots” into which users insert various “lexical substitutions" (Hartmann and Ungerer).

Algorithmic systems are increasingly being adopted by cultural heritage institutions like libraries. In this study, we investigate U.S. public libraries’ adoption of one specific automated tool – automated collection diversity audits – which we see as an illuminating case study for broader trends. Typically developed and sold by commercial book distributors, automated diversity audits aim to evaluate how well library collections reflect demographic and thematic diversity. We investigate how these audits function, whether library workers find them useful, and what is at stake when sensitive, normative decisions about representation are outsourced to automated commercial systems. Our analysis draws on an anonymous survey of U.S. public librarians (n=99), interviews with 14 librarians, a sample of purchasing records, and vendor documentation. We find that many library workers view these tools as convenient, time-saving solutions for assessing and diversifying collections under real and increasing constraints. Yet at the same time, the audits often flatten complex identities into standardized categories, fail to reflect local community needs, and further entrench libraries’ infrastructural dependence on vendors. We conclude with recommendations for improving collection diversity audits and reflect on the broader implications for public libraries operating at the intersection of AI adoption, escalating anti-DEI backlash, and politically motivated defunding.

This paper presents a set of provocations for considering the uses, impact, and harms of generative AI from the perspective of humanities researchers. We provide a working definition of humanities research, summarize some of its most salient theories and methods, and apply these theories and methods to the current landscape of AI. Drawing from foundational work in critical data studies, along with relevant humanities scholarship, we elaborate eight claims with broad applicability to current conversations about generative AI: 1) Models make words, but people make meaning; 2) Generative AI requires an expanded definition of culture; 3) Generative AI can never be representative; 4) Bigger models are not always better models; 5) Not all training data is equivalent; 6) Openness is not an easy fix; 7) Limited access to compute enables corporate capture; and 8) AI universalism creates narrow human subjects. We conclude with a discussion of the importance of resisting the extraction of humanities research by computer science and related fields.

Generating poetry has become a popular application of LLMs, perhaps especially of OpenAI’s widely-used chatbot ChatGPT. What kind of poet is ChatGPT? Does ChatGPT have its own poetic style? Can it successfully produce poems in different styles? To answer these questions, we prompt the GPT-3.5 and GPT-4 models to generate English-language poems in 24 different poetic forms and styles, about 40 different subjects, and in response to 3 different writing prompt templates. We then analyze the resulting 5.7k poems, comparing them to a sample of 3.7k poems from the Poetry Foundation and the Academy of American Poets. We find that the GPT models, especially GPT-4, can successfully produce poems in a range of both common and uncommon English-language forms in superficial yet noteworthy ways, such as by producing poems of appropriate lengths for sonnets (14 lines), villanelles (19 lines), and sestinas (39 lines). But the GPT models also exhibit their own distinct stylistic tendencies, both within and outside of these specific forms. Our results show that GPT poetry is much more constrained and uniform than human poetry, showing a strong penchant for rhyme, quatrains (4-line stanzas), iambic meter, first-person plural perspectives (we, us, our), and specific vocabulary like “heart,” “embrace,” “echo,” and “whisper.”

Large language models (LLMs) can now generate and recognize poetry. But what do LLMs really know about poetry? We develop a task to evaluate how well LLMs recognize one aspect of English-language poetry—poetic form—which captures many different poetic features, including rhyme scheme, meter, and word or line repetition. By using a benchmark dataset of over 4.1k human expert-annotated poems, we show that state-of-the-art LLMs can successfully identify both common and uncommon fixed poetic forms—such as sonnets, sestinas, and pantoums—with surprisingly high accuracy. However, performance varies significantly by poetic form; the models struggle to identify unfixed poetic forms, especially those based on topic or visual features. We additionally measure how many poems from our benchmark dataset are present in popular pretraining datasets or memorized by GPT-4, finding that pretraining presence and memorization may improve performance on this task, but results are inconclusive. We release a benchmark evaluation dataset with 1.4k public domain poems and form annotations, results of memorization experiments and data audits, and code.