Using these corpora to run several hundreds of experiments, two types of different classificatory features are tested: a rather short, previously selected list of multi-element function words and a large list with all word unigrams in a given corpus. The present study uses contributions to organized crime-related online forums to randomly create a number of corpora with an increasing number of subjects.
Easy to tag by computational tools, these multi-element units can produce long lists of features even in the small corpora –text collections authored by some set of subjects– which are standard in authorship attribution. More recently, the number of features used in authorship attribution has exploded as all sorts of multi-element units have been introduced in the task, such as character, word and POS n-grams (Rico Sulayes, 2014).
In order to achieve this goal, a constant proposal of new classificatory features has characterized the research on this task for a number of decades (Rudman, 1998). Frequently performed by means of automated processes, the task of authorship attribution is aimed at assigning an anonymous piece of text to a subject within a list of potential authors. The use of multi-element units in computational linguistics has a long tradition that is reflected on both automatic text classification and the related forensic linguistic task of authorship attribution.
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