# Burrows' Delta

> Measures how alike two texts are in their use of the most common words, so you can tell which texts were probably written by the same hand or in the same…

Source: https://mysteriesdept.com/methods/MTH08JFXFB81N4X/burrows-delta  
Publisher: Mysteries Department  
Licence: CC BY 4.0 (credit Mysteries Department and link the page)  

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[Methods](https://mysteriesdept.com/methods)[Stylometry](https://mysteriesdept.com/methods?kind=authorship-and-hands)Burrows' Delta

Method · stylometry

# Burrows' Delta

Measures how alike two texts are in their use of the most common words, so you can tell which texts were probably written by the same hand or in the same language.

**Stylometry** › authorshipVersion **1.0** Licence **MIT18** runsUsed on **1** mystery

## Questions it answers

- Which pages or texts were written by the same scribe?
- Do sections of a text group by subject, by hand, or by language?
- Is an unknown text closer to one candidate corpus than another?

## How it works

Take the most frequent words across all the texts. For each text, turn each word's frequency into a z-score against the whole corpus. Delta between two texts is the mean absolute difference of those z-scores. Small Delta means similar style.

Δ(a, b) = (1/n) Σ | za(wᵢ) − zb(wᵢ) |

**Assumes**
- Texts long enough to give stable word frequencies
- The same transcription and word boundaries throughout

**Watch out for**
- Topic can masquerade as authorship; control for subject
- Results depend on how many words you keep

## Where it comes from

Built on

**Function-word frequencies** Mosteller and Wallace, 1964

**Standard score (z-score)** Theory · statistics

**Burrows' Delta** Burrows, 2002

Led to

**Cosine Delta** Smith and Aldridge, 2011

**Zeta** Related: distinctive words

## Code Python 3.11

```
from mysteriesdept.methods import burrows_delta

tokens = load_dataset("voynich-token-table") # one row per word
pages = tokens.groupby("page")["word"].apply(list)

delta = burrows_delta(pages, n_words=150) # pages × pages matrix
nearest = delta.nearest(k=1) # each page's closest page

```

[Download module (.py)](https://mysteriesdept.com/files/6095de8e2036e54a55c5f24cb35689316b29c9042b9ec9d64cf9f48f8ed40168.py)`methods_ind.py`[View source](https://mysteriesdept.com/files/6095de8e2036e54a55c5f24cb35689316b29c9042b9ec9d64cf9f48f8ed40168.py)

## Runs and results 18 runs · 6 shown

| Run | Mystery | Measured | Result | Against | Date |
|---|---|---|---|---|---|
| RUN-226 | Voynich | Pages compared | 132.0 | Latin 132.0 · slot 132.0 · shuffled 132.0 | 22 Oct |
| RUN-225 | Voynich | Clusters follow the quires | 0.28 | Latin 0.206 · slot 0.058 · shuffled 0.102 | 22 Oct |
| RUN-224 | Voynich | Clusters follow the scribes | 0.231 | Latin 0.126 · slot 0.04 · shuffled 0.056 | 22 Oct |
| RUN-223 | Voynich | Clusters follow Currier's languages | 0.158 | Latin 0.039 · slot 0.053 · shuffled 0.083 | 22 Oct |
| RUN-222 | Voynich | Clusters follow the sections | 0.424 | Latin 0.266 · slot 0.028 · shuffled 0.041 | 22 Oct |
| RUN-221 | Voynich | Nearest page is in the same quire | 0.485 | Latin 0.394 · slot 0.212 · shuffled 0.242 | 22 Oct |

## Applied to

![Voynich Manuscript](https://mysteriesdept.com/media/folio/78r)

**[Voynich Manuscript](https://mysteriesdept.com/mysteries/voynich-manuscript)** Pages group by scribe and by Currier language more strongly than Latin texts group by author.

**3 datasets** Exploration split, Latin genre texts, ZL3b transcription

## References

1. [Burrows, J. 2002. 'Delta': a Measure of Stylistic Difference and a Guide to Likely Authorship. Literary and Linguistic Computing.](https://mysteriesdept.com/sources/SRC0D8MQACR578Y/delta-a-measure-of-stylistic-difference-and-a-guide-to)
1. [Smith, P., Aldridge, W. 2011. Improving Authorship Attribution: Optimizing Burrows' Delta Method. Journal of Quantitative Linguistics.](https://mysteriesdept.com/sources/SRC0D8MTSFE10DS/improving-authorship-attribution-optimizing-burrows-delta)
1. [Evert, S., Jannidis, F., Proisl, T. et al. 2017. Understanding and explaining Delta measures for authorship attribution. Digital Scholarship in the Humanities.](https://mysteriesdept.com/sources/SRC0D8MTSQ814JY/understanding-and-explaining-delta-measures-for-authorship)

[Download module](https://mysteriesdept.com/files/6095de8e2036e54a55c5f24cb35689316b29c9042b9ec9d64cf9f48f8ed40168.py)

Category

Stylometry

Version

1.0 · 22 Oct 2026

Licence

MIT

Language

Python

Inputs

Texts as word lists

Output

Distance matrix

Cite

[BibTeX](https://mysteriesdept.com/methods/MTH08JFXFB81N4X/burrows-delta/cite.bib) · [APA](https://mysteriesdept.com/methods/MTH08JFXFB81N4X/burrows-delta/cite.txt)

Develop this method

Methods are versioned like code. Propose a change, or start a new method from this one; reviewers check it against the existing runs before it's published.

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Authors

**[apollo](https://mysteriesdept.com/u/apollo)** Wrote version 1.0

JB

**John Burrows** Original method, 2002