Quantum mechanics

Linguistics

Philosophy

Sophistication

Attribution

Pure mathematics

Authorship attribution

Social psychology

Field (mathematics)

Power (physics)

Set (abstract data type)

Data mining

Computer science

Natural language processing

Physics

Psychology

Mathematics

Astronomy

Value (mathematics)

Sociology

Reliability (semiconductor)

Measure (data warehouse)

Programming language

Delta

Machine learning

Social science

History

Literary and Linguistic Computing

This paper is a companion to my ‘Questions of authorship: attribution and beyond’, in which I sketched a new way of using the relative frequencies of the very common words for comparing written texts and testing their likely authorship. The main emphasis of that paper was not on the new procedure but on the broader consequences of our increasing sophistication in making such comparisons and the increasing (although never absolute) reliability of our inferences about authorship. My present objects, accordingly, are to give a more complete account of the procedure itself; to report the outcome of an extensive set of trials; and to consider the strengths and limitations of the new procedure. The procedure offers a simple but comparatively accurate addition to our current methods of distinguishing the most likely author of texts exceeding about 1,500 words in length. It is of even greater value as a method of reducing the field of likely candidates for texts of as little as 100 words in length. Not unexpectedly, it works least well with texts of a genre uncharacteristic of their author and, in one case, with texts far separated in time across a long literary career. Its possible use for other classificatory tasks has not yet been investigated.

'Delta': a Measure of Stylistic Difference and a Guide to Likely Authorship