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MethodsStylometryBurrows' 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 1.0Licence 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 frequenciesMosteller and Wallace, 1964
Standard score (z-score)Theory · statistics
Burrows' DeltaBurrows, 2002
Led to
Cosine DeltaSmith and Aldridge, 2011
ZetaRelated: 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

Runs and results 18 runs · 6 shown

RunMysteryMeasuredResultAgainstDate
RUN-226VoynichPages compared132.0Latin 132.0 · slot 132.0 · shuffled 132.022 Oct
RUN-225VoynichClusters follow the quires0.28Latin 0.206 · slot 0.058 · shuffled 0.10222 Oct
RUN-224VoynichClusters follow the scribes0.231Latin 0.126 · slot 0.04 · shuffled 0.05622 Oct
RUN-223VoynichClusters follow Currier's languages0.158Latin 0.039 · slot 0.053 · shuffled 0.08322 Oct
RUN-222VoynichClusters follow the sections0.424Latin 0.266 · slot 0.028 · shuffled 0.04122 Oct
RUN-221VoynichNearest page is in the same quire0.485Latin 0.394 · slot 0.212 · shuffled 0.24222 Oct

Applied to

Voynich Manuscript
Voynich ManuscriptPages group by scribe and by Currier language more strongly than Latin texts group by author.
3 datasetsExploration 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.
  2. Smith, P., Aldridge, W. 2011. Improving Authorship Attribution: Optimizing Burrows' Delta Method. Journal of Quantitative Linguistics.
  3. Evert, S., Jannidis, F., Proisl, T. et al. 2017. Understanding and explaining Delta measures for authorship attribution. Digital Scholarship in the Humanities.
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