description of the style of each author is in general subjective, and therefore 13 hard to derive in natural language; it is even harder to find a description 14 which enables a machine to automatically tell one author from the other. A 15 literature review on modern authorship attribution methods, usually coming 16 from the fields of machine learning and statistical analysis, is reported in 17 Stamatatos (2009); Jockers and Witten (2010); Koppel et al. (2009); Grieve 18 (2007); Juola (2006). Among these, algorithms based on similarity measures 19 such as Benedetto et al. (2002) and Koppel et al. (2011) are widely employed 20 and usually assign an anonymous text to the author of the most similar 21 document in the training data. 22 During the last decade, compression-based distance measures have been 23 effectively applied to cluster texts written by different authors (Cilibrasi and Vit´ anyi, 24 2005) and to perform plagiarism detection (Chen et al., 2004). Such univer- 25 sal similarity measures, of which the most well-known is the Normalized 26 Compression Distance (NCD), employ general compressors to estimate the 27 amount of shared information between two objects. Similar concepts are 28 also used by methods using runlength histograms to retrieve and classify 29 documents (Gordo et al., 2013). Experiments carried out in Oliveira et al. 30 (2013) conclude that NCD-based methods for authorship analysis outper- 31 form state-of-the-art classification methodologies such as Support Vector 32 Machines. A study on larger and more statistically meaningful datasets 33 shows NCD-methods to be competitive with respect to the state of the art 34 (de Graaff, 2012), while Stamatatos (2009) reports that compression-based 35 2