Narrative Fingerprints: Multi-Scale Author Identification via Novelty Curve Dynamics

TL;DR

This paper investigates whether authors have unique fingerprints in the way novelty evolves in their texts, using information-theoretic measures across large corpora to identify authors with high accuracy.

Contribution

The study introduces a novel approach analyzing novelty curve dynamics at multiple scales for author identification, demonstrating significant attribution accuracy.

Findings

Scalar novelty features identify 43% of authors above chance.

SAX motif patterns achieve 30x-above-chance attribution at chapter level.

Author fingerprints persist within genres and are present in classical authors.

Abstract

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.