Estimating the probability of an authorship attribution

In authorship attribution, various distance‐based metrics have been proposed to determine the most probable author of a disputed text. In this paradigm, a distance is computed between each author profile and the query text. These values are then employed only to rank the possible authors. In this article, we analyze their distribution and show that we can model it as a mixture of 2 Beta distributions. Based on this finding, we demonstrate how we can derive a more accurate probability that the closest author is, in fact, the real author. To evaluate this approach, we have chosen 4 authorship attribution methods (Burrows' Delta, Kullback‐Leibler divergence, Labbé's intertextual distance, and the naïve Bayes). As the first test collection, we have downloaded 224 State of the Union addresses (from 1790 to 2014) delivered by 41 U.S. presidents. The second test collection is formed by the Federalist Papers. The evaluations indicate that the accuracy rate of some authorship decisions can be improved. The suggested method can signal that the proposed assignment should be interpreted as possible, without strong certainty. Being able to quantify the certainty associated with an authorship decision can be a useful component when important decisions must be taken.