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Authorship Attribution using Compression Distances

Authorship attribution has been a field of interest for researchers in the past, especially for forensic purposes. In this thesis, to obtain the degree of Bachelor of Science from the Leiden University, we investigate character n-grams and so-called compression distances to prototypes on several datasets, i.e., the datasets provided by PAN Labs (a benchmarking activity on uncovering plagiarism, authorship and social software misuse). This research was done in cooperation with the Netherlands Forensic Institute (NFI), who supervised this research as well. Compression distances to randomly selected prototypes from the training-corpus documents are used as feature representation, which has shown to be an elegant and powerful method in authorship attribution on large numbers of training documents. The standard compression distances to prototypes is not applicable to a dataset with a small number of samples and authors. We propose Bootstrapped Authorship Attribution in Compression Space (BAACS), an adaption of the standard compression distances to prototypes, to attribute authorship for datasets with a small number of samples and authors. BAACS is based on bootstrapping, a resampling method, to enrich the distribution of the dataset with more samples trying to generalize error estimation. We draw prototypes without replacement and in order to enrich the dataset with more samples, we draw samples with replacement from the small number of source documents per author. For the open class recognition tasks, we additionally set a threshold for the minimum posterior probability for any of the known classes.