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Neel Gupta

@neel2112.bsky.social
244 followers 479 following 10 posts

PhD Student at UW iSchool researching cultural analytics. neelgupta2112.github.io

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Neel Gupta @neel2112.bsky.social · 24/06/2026
Preprint with @mariaa.bsky.social and @mellymeldubs.bsky.social on how real people are writing fiction with LLMs based on the Wildchat dataset here arxiv.org/abs/2606.22748.
arxiv.org
AI Fiction in the Wild
Some professional authors are beginning to use AI tools to help produce their fiction writing. Are readers using AI to generate fiction, too? Drawing on over 500,000 anonymized, English-language ChatG...
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Neel Gupta @neel2112.bsky.social · 26/03/2026
Had fun working on two new datasets for the Responsible Datasets in Context project. First, I'm excited to share a dataset of US governors from 1775-present which was put together by a group of political scientists based out of Yale. www.responsible-datasets-in-context.com/posts/gubern...
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Neel Gupta @neel2112.bsky.social · 11/12/2025
Excited to be in Luxembourg at CHR 2025 to hear about everyone’s amazing work and to share my project with @mellymeldubs.bsky.social and our team. We tracked canonical authors and texts in Seattle Public Library circulation data.
Graphs showing authors from the Norton Anthology receiving checkout spikes after their death.
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Neel Gupta @neel2112.bsky.social · 18/08/2025
David Christensen, @mellymeldubs.bsky.social & I wrote about the Seattle Public Library open checkout dataset. The data is idiosyncratic and imperfect—but also a rare, detailed look at book popularity over time. Excited to see how others put it to use! openhumanitiesdata.metajnl.com/articles/10....
openhumanitiesdata.metajnl.com
Seattle Public Library’s Open Checkout Data: What Can It Tell Us About Readers and Book Popularity More Broadly? | Journal of Open Humanities Data
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?
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