Stylometric Fingerprinting

Overview

Extract a stable stylometric fingerprint from a text corpus by measuring function word frequencies, sentence structure distributions, and punctuation patterns -- the features that remain consistent across topics and time periods because they are produced unconsciously. The core principle: function words and syntactic patterns are more stable authorship markers than content words because content words change with topic while function words reflect deeply ingrained grammatical habits (Mosteller & Wallace, 1963; Kestemont, 2014). The fingerprint is a distributional profile, not a set of point estimates -- each feature is characterized by its mean, standard deviation, and cross-context stability coefficient.

Research foundation: Function words have been the backbone of computational stylometry since the Federalist Papers attribution (Mosteller & Wallace, 1963). Argamon & Levitan (2005) demonstrated that function word frequencies alone achieve 80%+ accuracy in authorship attribution across genres. Eder (2011) established that 2,500+ words are needed for reliable frequency estimation with the 100 most frequent words, while Stamatatos (2009) showed that character n-grams and function word distributions are among the most robust features for cross-domain authorship identification.

When to Use

• Extracting an author's stable writing signature from a corpus of their text
• Building a style profile for writing voice replication that survives topic changes
• Defining syntactic constraints that characterize an author's habitual patterns
• Comparing writing style consistency across contexts, time periods, or platforms
• Creating a feature set for authorship verification or attribution tasks
• Feeding stable stylometric features into a persona or archetype system

When NOT to use: