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Fast Stylometry

Authors: Wood, Thomas;

Fast Stylometry

Abstract

Fast Stylometry is a Python library for calculating the Burrows' Delta. Burrows' Delta is an algorithm for comparing the similarity of the writing styles of documents, known as forensic stylometry. The library can also calculate the probability that two books were by the same author. I wrote this library to improve my understanding, and also because the existing libraries I could find were focused around generating graphs but did not go as far as calculating probabilities. Burrows' Delta algorithm The Burrows' delta is a statistic which expresses the distance between two authors' writing styles. A high number like 3 implies that the two authors are very dissimilar, whereas a low number like 0.2 would imply that two books are very likely to be by the same author. Explanation of the maths and thinking behind Burrows' Delta and how it works. The Burrows' delta is calculated by comparing the relative frequencies of function words such as “inside”, “and”, etc, in the two texts, taking into account their natural variation between authors.

Keywords

machine learning, burrows delta, stylometry, data science, natural language processing, nlp, authorship analysis

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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