Stylometry


Stylometry is the application of the study of linguistic style, usually to written language, but it has successfully been applied to music and to fine-art paintings as well. Another conceptualization defines it as the linguistic discipline that applies statistical analysis to literature by evaluating the author's style through various quantitative criteria.
Stylometry is often used to attribute authorship to anonymous or disputed documents. It has legal as well as academic and literary applications, ranging from the question of the authorship of Shakespeare's works to forensic linguistics.

History

Stylometry grew out of earlier techniques of analyzing texts for evidence of authenticity, author identity, and other questions.
The modern practice of the discipline received major impetus from the study of authorship problems in English Renaissance drama. Researchers and readers observed that some playwrights of the era had distinctive patterns of language preferences, and attempted to use those patterns to identify authors in uncertain or collaborative works. Early efforts were not always successful: in 1901, one researcher attempted to use John Fletcher's preference for "⁠ ⁠’em", the contractional form of "them", as a marker to distinguish between Fletcher and Philip Massinger in their collaborations—but he mistakenly employed an edition of Massinger's works in which the editor had expanded all instances of "⁠ ⁠’em" to "them".
The basics of stylometry were set out by Polish philosopher Wincenty Lutosławski in Principes de stylométrie. Lutosławski used this method to build a chronology of Plato's Dialogues.
The development of computers and their capacities for analyzing large quantities of data enhanced this type of effort by orders of magnitude. The great capacity of computers for data analysis, however, did not guarantee quality output. In the early 1960s, Rev. A. Q. Morton produced a computer analysis of the fourteen Epistles of the New Testament attributed to St. Paul, which showed that six different authors had written that body of work. A check of his method, applied to the works of James Joyce, gave the result that Ulysses, Joyce's multi-perspective, multi-style masterpiece, was written by five separate individuals, none of whom apparently had any part in the crafting of Joyce's first novel, A Portrait of the Artist as a Young Man.
In time, however, and with practice, researchers and scholars have refined their approaches and methods, to yield better results. One notable early success was the resolution of disputed authorship in twelve of The Federalist Papers by Frederick Mosteller and David Wallace.
While questions of initial assumptions and methodology still arise, few now dispute the basic premise that linguistic analysis of written texts can produce valuable information and insight.

Applications

Applications of stylometry include literary studies, historical studies, social studies, gender studies, and many forensic cases and studies. It can also be applied to computer code. Stylometry can also be used to predict whether someone is a native or non native English speaker through their typing speed.
Stylometry as a method is vulnerable to the distortion of text during revision. There is also the case of the author adopting different styles in the course of his career as was demonstrated in the case of Plato, who chose different stylistic policies such as the those adopted for the early and middle dialogues addressing the Socratic problem.

Current research

Modern stylometry draws heavily on the aid of computers for statistical analysis, artificial intelligence and access to the growing corpus of texts available via the Internet. Software systems such as Signature, JGAAP, stylo and Stylene for Dutch make its use increasingly practicable, even for the non-expert.

Academic venues and events

Stylometric methods are discussed in several academic fields, mostly as a tangential field of application as with machine learning, natural language processing, and lexicography.

Forensic linguistics

The International Association of Forensic Linguists organises the Biennial Conference of the International Association of Forensic Linguists and publishes The International Journal of Speech, Language and the Law with forensic stylistics as one of its central topics.

AAAI

The Association for the Advancement of Artificial Intelligence has hosted several events on subjective and stylistic analysis of text.

PAN

PAN workshops organised since 2007 mainly in conjunction with information access conferences such as ACM SIGIR, FIRE, and CLEF. PAN formulates shared challenge tasks for plagiarism detection, authorship identification, author gender identification, author profiling, vandalism detection, and other related text analysis tasks, many of which hinge on stylometry.

Case studies of interest

Since stylometry has both descriptive use cases, used to characterise the content of a collection, and identificatory use cases, e.g. identifying authors or categories of texts, the methods used to analyse the data and features above range from those built to classify items into sets or to distribute items in a space of feature variation. Most methods are statistical in nature, such as cluster analysis and discriminant analysis, are typically based on philological data and features, and are fruitful application domains for modern machine learning approaches.
Whereas in the past, stylometry emphasized the rarest or most striking elements of a text, contemporary techniques can isolate identifying patterns even in common parts of speech. Most systems are based on lexical statistics, i.e. using the frequencies of words and terms in the text to characterise the text. In this context, unlike in information retrieval, the observed occurrence patterns of the most common words are more interesting than the topical terms which are less frequent.
The primary stylometric method is the writer invariant: a property held in common by all texts, or at least all texts long enough to admit of analysis yielding statistically significant results, written by a given author. An example of a writer invariant is frequency of function words used by the writer.
In one such method, the text is analyzed to find the 50 most common words. The text is then broken into 5,000 word chunks and each of the chunks is analyzed to find the frequency of those 50 words in that chunk. This generates a unique 50-number identifier for each chunk. These numbers place each chunk of text into a point in a 50-dimensional space. This 50-dimensional space is flattened into a plane using principal components analysis. This results in a display of points that correspond to an author's style. If two literary works are placed on the same plane, the resulting pattern may show if both works were by the same author or different authors.

1. Gaussian statistics

Stylometric data are distributed according to the Zipf-Mandelbrot law. The distribution is extremely spiky and leptokurtic, reason why researchers had to turn their backs to statistics to solve e.g. authorship attribution problems. Nevertheless, usage of Gaussian statistics is perfectly possible by applying data transformation.

2. Neural networks

s, a special case of statistical machine learning methods, have been used to analyze authorship of texts. Text of undisputed authorship are used to train the neural network through processes such as backpropagation, where training error is calculated and used to update the process to increase accuracy. Through a process akin to non-linear regression, the network gains the ability to generalize its recognition ability to new texts to which it has not yet been exposed, classifying them to a stated degree of confidence. Such techniques were applied to the long-standing claims of collaboration of Shakespeare with his contemporaries Fletcher and Christopher Marlowe, and confirmed the view, based on more conventional scholarship, that such collaboration had indeed taken place.
A 1999 study showed that a neural network program reached 70% accuracy in determining authorship of poems it had not yet analyzed. This study from Vrije Universiteit examined identification of poems by three Dutch authors using only letter sequences such as "den".
A study used deep belief networks for authorship verification model applicable for continuous authentication.
One problem with this method of analysis is that the network can become biased based on its training set, possibly selecting authors the network has more often analyzed.

3. Genetic algorithms

The genetic algorithm is another machine learning technique used in stylometry. This involves a method that starts out with a set of rules. An example rule might be, "If but appears more than 1.7 times in every thousand words, then the text is author X". The program is presented with text and uses the rules to determine authorship. The rules are tested against a set of known texts and each rule is given a fitness score. The 50 rules with the lowest scores are thrown out. The remaining 50 rules are given small changes and 50 new rules are introduced. This is repeated until the evolved rules correctly attribute the texts.

4. Rare pairs

One method for identifying style is called "rare pairs", and relies upon individual habits of collocation. The use of certain words may, for a particular author, idiosyncratically entail the use of other, predictable words.

Authorship attribution in instant messaging

The diffusion of Internet has shifted the authorship attribution attention towards online texts electronic messages, and other types of written information that are far shorter than an average book, much less formal and more diverse in terms of expressive elements such as colors, layout, fonts, graphics, emoticons, etc. Efforts to take into account such aspects at the level of both structure and syntax were reported in. In addition, content-specific and idiosyncratic cues were introduced to unveil deliberate stylistic choices.
Standard stylometric features have been employed to categorize the content of a chat over instant messaging, or the behavior of the participants, but attempts of identifying chat participants are still few and early. Furthermore, the similarity between spoken conversations and chat interactions has been neglected while being a key difference between chat data and any other type of written information.