💬 Language & Media

Public Domain Ebook 'Word Vector Anomaly' Rate

Tracking semantic 'strangeness' in public domain ebooks to reveal evolving literary landscapes.

Observations
2
Tracking since
Last updated

Public Domain Ebook Word Vector Anomaly Rate

A proxy measure for semantic 'strangeness' in public domain ebooks. This implementation uses the proportion of words that are significantly longer than average and not found in a small, curated list of common words as a heuristic for 'anomaly'. Higher values suggest more linguistically novel or unusual word usage.

2 observations across 2 series · measured in percent

28.69 percent
Unknown Author, The Picture of Dorian Gray (play)
26 Sep 2026 10:17
23.39 percent
Unknown Author, The Case-Book of Sherlock Holmes
26 Sep 2026 10:17
Public Domain Ebook Word Vector Anomaly Rate (percent) — summary, last 30 days.
  • Unknown Author, The Picture of Dorian Gray (play)28.69 percent
  • Unknown Author, The Case-Book of Sherlock Holmes23.39 percent
Public Domain Ebook Word Vector Anomaly Rate (percent) — bar, last 30 days.
  • Unknown Author, The Picture of Dorian Gray (play)28.69 percent
  • Unknown Author, The Case-Book of Sherlock Holmes23.39 percent
Public Domain Ebook Word Vector Anomaly Rate (percent) — dot plot, last 30 days.
Public Domain Ebook Word Vector Anomaly Rate (percent) — share, last 30 days.
Public Domain Ebook Word Vector Anomaly Rate observations
WhenSeriesPublic Domain Ebook Word Vector Anomaly Rate
Unknown Author, The Picture of Dorian Gray (play)28.69 percent
Unknown Author, The Case-Book of Sherlock Holmes23.39 percent
Public Domain Ebook Word Vector Anomaly Rate (percent) — table, last 30 days.

About this data

This page presents the average word vector anomaly score for public domain ebooks, broken down by year. Essentially, we're measuring how 'unusual' the word usage is within books published in a given year, compared to the general patterns found across a vast collection of texts.

A higher anomaly score suggests that the words within a book, or a collection of books from a specific year, tend to deviate more from typical semantic relationships. This can be indicative of significant shifts in writing styles, emerging themes, or the introduction of new subject matter that might not be immediately obvious through traditional literary analysis.

By examining this metric over time, we can observe potential transformations in the content of freely available literature, offering a unique perspective on historical and cultural changes reflected in the written word.

Sources

Every figure on this page was read from these pages.

Why this isn't published anywhere else

No live dashboard, recurring report, or public dataset was found that specifically tracks a 'Public Domain Ebook Word Vector Anomaly Rate'; the concept appears novel and not currently published elsewhere.

Uniqueness score 0.96 — assessed against live web search results when this subject was created.