CHiPS: Character Histograms and Positional Signals for Lightweight Authorship Attribution in Romanian Texts

• URL: http://arxiv.org/abs/2607.22884v1
• Date: Fri, 24 Jul 2026 19:48:32 GMT
• Title: CHiPS: Character Histograms and Positional Signals for Lightweight Authorship Attribution in Romanian Texts
• Abstract summary: CHiPS is a character-level authorship attribution method for Romanian texts.<n>CHiPS studies two complementary fingerprints of writing style.<n>CHiPS-F is a leakage-safe decision-level fusion variant.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose CHiPS, a lightweight character-level authorship attribution method for Romanian texts. All reported experiments are closed-set: the true author is one of the candidate authors in the training data. CHiPS studies two complementary fingerprints of writing style: CH-SVM, a character-histogram classifier based on one-character marginal distributions, and FFT12-LR, a positional-signal classifier that represents selected characters and punctuation classes as impulse trains (binary indicator sequences over character positions) and extracts Fourier/Welch spectral descriptors. We also report CHiPS-F, a leakage-safe decision-level fusion variant, and an optional top-5 listwise reranker trained only on out-of-fold predictions. The method requires no tokenization, syntactic analysis, pretrained language model, or transformer fine-tuning, and it avoids character $n$-gram features with $n \geq 2$ in the histogram component. On a locked grouped ROST split comprising 400 files from 392 source-text groups, written by 10 authors, with source-text-level evaluation and grouped five-fold model selection, CHiPS-F reaches 0.9310 accuracy and 0.9341 macro-F1. A matched but unrestricted character 2--5-gram TF--IDF SVM comparator reaches 1.0000 accuracy and macro-F1 on the same held-out groups, so the contribution is not a claim of best possible classification accuracy. Instead, the experiments ask how far restricted, transparent character evidence can go under strict leakage control. On ROSTories-cleaned, a secondary ROST-overlapping corpus comprising 1,248 files from 1,240 source-text groups, written by 19 authors, the same protocol gives 0.8919 accuracy and 0.8708 macro-F1 for CHiPS-R.

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