An open‐set size‐adjusted B ayesian classifier for authorship attribution
G. Bruce Schaalje, Natalie J. Blades, Tomohiko Funai · Journal of the American Society for Information Science and Technology · 2013
Recent studies of authorship attribution have used machine‐learning methods including regularized multinomial logistic regression, neural nets, support vector machines, and the nearest shrunken centroid classifier to identify likely authors of disputed texts. These methods are all limited by an inability to perform open‐set classification and account for text and corpus size. We propose a customizedBayesian logit‐normal‐beta‐binomial classification model for supervised authorship attribution. The model is based on the beta‐binomial distribution with an explicit inverse relationship between extra‐binomial variation and text size. The model internally estimates the relationship of extra‐binomial variation to text size, and usesMarkovChainMonteCarlo (MCMC) to produce distributions of posterior authorship probabilities instead of point estimates. We illustrate the method by training the machine‐learning methods as well as the open‐setBayesian classifier on undisputed papers ofThe Federalist, and testing the method on documents historically attributed toAlexanderHamilton,JohnJay, andJamesMadison. TheBayesian classifier was the best classifier of these texts.