Narrative Fingerprints: Multi-Scale Author Identification via Novelty Curve Dynamics
This addresses the problem of author identification for literary analysis and forensics, but it is incremental as it builds on existing novelty curve methods.
The study investigated whether authors have distinctive 'fingerprints' in the information-theoretic novelty curves of their works, finding that authorial voice leaves measurable traces across texts, with multi-scale signals achieving up to 30x-above-chance attribution at the chapter level.
We test whether authors have characteristic "fingerprints" in the information-theoretic novelty curves of their published works. Working with two corpora -- Books3 (52,796 books, 759 qualifying authors) and PG-19 (28,439 books, 1,821 qualifying authors) -- we find that authorial voice leaves measurable traces in how novelty unfolds across a text. The signal is multi-scale: at book level, scalar dynamics (mean novelty, speed, volume, circuitousness) identify 43% of authors significantly above chance; at chapter level, SAX motif patterns in sliding windows achieve 30x-above-chance attribution, far exceeding the scalar features that dominate at book level. These signals are complementary, not redundant. We show that the fingerprint is partly confounded with genre but persists within-genre for approximately one-quarter of authors. Classical authors (Twain, Austen, Kipling) show fingerprints comparable in strength to modern authors, suggesting the phenomenon is not an artifact of contemporary publishing conventions.