Exploring the Potential of Bootstrap Consensus Networks for Large-scale Authorship Attribution in Luxdorph’s Freedom of the Press Writings

Authorship attribution (AA) is concerned with the task of finding out about the true authorship of a disputed text based on a set of documents of known authorship. In this paper, we investigate the potential of Bootstrap Consensus Networks (BCN) – a novel approach to generate visualizations in stylometry by mapping similarities of authorial style between texts into the form of a network – for large-scale authorship attribution tasks. We apply this method to the freedom of the press writings (Trykkefrihedsskrifter), a corpus of pamphlets published and collected in Denmark at the end of the 18th century. By conducting multiple experiments, we find that the size of the constructed networks depends heavily on the type of variables and distance measures used. Furthermore, we find that, although a small set of unknown authorship problems can be solved, in general, the precision of the BCN method is too low to apply it in a large-scale AA scenario.