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Quantifying AI in stories

2026-06-29 18:39:02.527198+02 by Dan Lyke 0 comments

StoryScope: Investigating idiosyncrasies in AI fiction (Preprint) Jenna Russell, Rishanth Rajendhran, Chau Minh Pham, Mohit Iyyer, John Wieting

A compact set of 30 core narrative features captures much of this signal: AI stories over-explain themes and favor tidy, single-track plots while human stories frame protagonist’ choices as more morally ambiguous and have increased temporal complexity (e.g., flashbacks, nonlinear structure). Per-model fingerprint features enable six-way attribution: for example, Claude produces notably flat event escalation, GPT over-indexes on dream sequences, and Gemini defaults to external character description. We find that AI-generated stories cluster in a shared region of narrative space, while human-authored stories exhibit greater diversity. More broadly, these results suggest that differences in underlying narrative construction, not just writing style, can be used to separate human-written original works from AI-generated fiction.

Via LinkedIn: The Shape of Enshittification: Books That No Longer Get Read, An Internet That No Longer Gets Surfed, & The End of Social Media As We Know It..., which is a pitch piece for Return to Real: The Last Human Advantage in an Age of Artificial Everything by Ryan Levesque, but which suffers from LinkedIn-ness so deeply that I can't tell if it's AI generated...

[ related topics: Interactive Drama Books Space & Astronomy Writing Journalism and Media Net Culture Machinery Fabrication Artificial Intelligence Model Building ]

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