# Higher Criticism for Discriminating Word-Frequency Tables and Testing Authorship

arXiv: https://arxiv.org/abs/1911.01208 · HTML: https://mlsanity.com/paper/1911.01208/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Sparse Signal Detection in Heteroscedastic Gaussian Sequence Models: Sharp Minimax Rates](https://mlsanity.com/paper/2211.08580/) (2022)
- [Critical biblical studies via word frequency analysis: unveiling text authorship](https://mlsanity.com/paper/2410.19883/) (2024)
- [From Small to Large Language Models: Revisiting the Federalist Papers](https://mlsanity.com/paper/2503.01869/) (2025)

## Abstract (arXiv)

We adapt the Higher Criticism (HC) goodness-of-fit test to measure the closeness between word-frequency tables. We apply this measure to authorship attribution challenges, where the goal is to identify the author of a document using other documents whose authorship is known. The method is simple yet performs well without handcrafting and tuning; reporting accuracy at the state of the art level in various current challenges. As an inherent side effect, the HC calculation identifies a subset of discriminating words. In practice, the identified words have low variance across documents belonging to a corpus of homogeneous authorship. We conclude that in comparing the similarity of a new document and a corpus of a single author, HC is mostly affected by words characteristic of the author and is relatively unaffected by topic structure.