This paper provides an analysis intended to aid model selection when developing a solution for an application-specific instance of authorship attribution. In this work, we replicate similarity-based methods with Integrated Syntactic Graphs (ISGs) presented by Gómez-Adorno et al. (2016). We also examine supervised learning with Support Vector Machines (SVMs) using a "locally-weighted bag of histograms" feature vector, following Escalante et al. (2011). The aim of this investigation is to evaluate the performance of both models on a range of corpora with varying characteristics, including the number of candidate authors, the number of documents per author, and the content type of each source. While overall performance was low, a negative trend with the number of authors is observed, which is not mitigated by increase in documents per author. Interesting results are seen when tests are run on mixed corpora.

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