Melanie Walsh

Assistant Professor | Information School | University of Washington | Seattle, USA 🌦️😎

Hi, I’m Melanie Walsh. I’m an Assistant Professor in the Information School and an Adjunct Assistant Professor in the English Department at the University of Washington. I’m also on the Executive Council for the new Center for Advances in Libraries, Archives, and Museums (CALMA).

Before UW, I was a Postdoctoral Associate in Information Science at Cornell University, where I worked with David Mimno. I received my PhD in English Literature from Washington University in St. Louis.

My research interests include data science, digital humanities/cultural analytics, literature, libraries, and social media—preferably all of the above combined. I investigate how data and computational methods shape contemporary culture — such as social media, the publishing industry, and public libraries — and how they can be used to understand culture in turn. You can download my full CV here.

I am currently at work on a book project, When Postwar American Fiction Went Viral: Protest, Profit, and Popular Readers in the 21st Century, which follows the social media afterlives of American authors.

I also designed and released a free, open-source textbook, Introduction to Cultural Analytics & Python, which introduces the programming language Python to people interested in the humanities and social science.

With wonderful collaborators, I also lead several projects that I’m proud of:

In my free time, I like to play basketball 🏀, hike ⛰️, and paddle board 🦭. I am not the children’s book author Melanie Walsh, but, confusingly, our interests seem to be converging.

news

Jun 23, 2025	I will be presenting our paper, “Algorithms in the Stacks: Investigating data-driven, for-profit diversity audits in public libraries,” at FAccT in Athens, Greece.
May 14, 2025	I will be speaking on the “Co-Intelligence” panel at the Society for Novel Studies Conference at Duke University.
May 14, 2025	I will be at Princeton for The Ends of Prosody conference.
Apr 25, 2025	I gave a talk, “What Do LLMs Know About Poetry?”, at the Humanities AI Conference at Lehigh University.
Apr 23, 2025	With Lauren Klein and Jessica Marie Johnson, I discussed our paper, “Provocations from the Humanities for Generative AI Research,” at the Reframing Resistance to AI Symposium.

selected publications

Provocations from the Humanities for Generative AI Research (Pre-Print)

Lauren Klein, Meredith Martin, André Brock, Maria Antoniak, Melanie Walsh, Jessica Marie Johnson, and 2 more authors

2025

arXiv Copy Bibtex Citation Read the Abstract

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.
@article{kleinProvocationsHumanitiesGenerative2025, title = {Provocations from the {{Humanities}} for {{Generative AI Research}} (Pre-Print)}, author = {Klein, Lauren and Martin, Meredith and Brock, André and Antoniak, Maria and Walsh, Melanie and Johnson, Jessica Marie and Tilton, Lauren and Mimno, David}, date = {2025-02-26}, year = {2025}, eprint = {2502.19190}, eprinttype = {arXiv}, eprintclass = {cs}, urldate = {2025-03-24}, pubstate = {prepublished}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Computers and Society}, file = {/Users/melwalsh/Zotero/storage/SN7FMYBN/Klein et al. - 2025 - Provocations from the Humanities for Generative AI.pdf;/Users/melwalsh/Zotero/storage/2VMLXASY/2502.html} }
Does ChatGPT Have a Poetic Style?

Melanie Walsh, Anna Preus, and Elizabeth Gronski

In Computational Humanities Research (CHR), Dec 2024

arXiv:2410.15299

arXiv PDF Code Copy Bibtex Citation Read the Abstract

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.”
@inproceedings{Walsh_Preus_Gronski_2024, title = {Does ChatGPT Have a Poetic Style?}, doi = {10.48550/arXiv.2410.15299}, chr = {https://ceur-ws.org/Vol-3834/}, note = {arXiv:2410.15299}, number = {arXiv:2410.15299}, publisher = {arXiv}, author = {Walsh, Melanie and Preus, Anna and Gronski, Elizabeth}, year = {2024}, month = dec, booktitle = {Computational Humanities Research (CHR)} }

Sonnet or Not, Bot? Poetry Evaluation for Large Models and Datasets

Melanie Walsh, Maria Antoniak, and Anna Preus

In Findings of the Association for Computational Linguistics: EMNLP, Nov 2024

ACL Anthology arXiv PDF Code & Data Copy Bibtex Citation Read the Abstract

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.

@inproceedings{Walsh_Antoniak_Preus_2024,
  title = {Sonnet or Not, Bot? Poetry Evaluation for Large Models and Datasets},
  abstractnote = {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.},
  acl = {https://aclanthology.org/2024.findings-emnlp.914/},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP},
  author = {Walsh, Melanie and Antoniak, Maria and Preus, Anna},
  year = {2024},
  month = nov,
  language = {en}
}

The Challenges and Possibilities of Social Media Data: New Directions in Literary Studies and the Digital Humanities

Melanie Walsh

Nov 2023

publisher: U of Minnesota Press

Debates in DH PDF Copy Bibtex Citation

@inbook{Walsh_2023,
  title = {The Challenges and Possibilities of Social Media Data: New Directions in Literary Studies and the Digital Humanities},
  url = {https://dhdebates.gc.cuny.edu/read/f3f87448-138c-4d19-8ff8-b06acf40ddd1/section/a57b98ab-0f10-45d0-b205-3e563aab7ea8#ch18},
  note = {publisher: U of Minnesota Press},
  booktitle = {Debates in the Digital Humanities 2023},
  publisher = {University of Minnesota Press},
  author = {Walsh, Melanie},
  year = {2023}
}

Where is all the book data?

Melanie Walsh

Public Books, Nov 2022

🎙️I spoke about this essay with Australian radio ABC.🎙️

Public Books Sydney Review of Books Interview

The Goodreads “Classics”: A Computational Study of Readers, Amazon, and Crowdsourced Amateur Criticism

Melanie Walsh, and Maria Antoniak

Post45 and Journal of Cultural Analytics, Nov 2021

Post45 Journal of Cultural Analytics PDF Interactive Plots Code & Data Goodreads Scraper Copy Bibtex Citation

@article{Walsh_Antoniak_2021,
  title = {The Goodreads “Classics”: A Computational Study of Readers, Amazon, and Crowdsourced Amateur Criticism},
  volume = {6},
  url = {https://culturalanalytics.org/article/22221.pdf},
  number = {2},
  journal = {Post45 and Journal of Cultural Analytics},
  author = {Walsh, Melanie and Antoniak, Maria},
  year = {2021},
  bibtex_show = true
}