Explainable Authorship Verification in Social Media via Attention-based Similarity Learning This work was in significant parts performed on a high performance computing cluster at Bucknell University through the support of the National Science Foundation, Grant Number 1659397.
TABLE I: Average verification error rates and standard deviations for the test set over a 5-fold cross-validation.
['labels', 'error rate in % Imposters', 'error rate in % AvEeer', 'error rate in % Glad', 'error rate in % Hrsn', 'error rate in % AdHominem']
Comparing the results of the baseline methods Imposters, Aveer and Glad, we obtained error rates between 27%−34%, while both, Hrsn and AdHominem, were able to cut the error rate in half to around 14%−16%. Comparing Hrsn and AdHominem, we were able to slightly increase the average accuracy with our new attention-based Siamese topology. Rows 2-5 additionally show the performance w.r.t. As it can be seen, all systems perform well for same-author/same-category instances as well as for different-author/cross-topic instances. As one would expect, the error rates for all methods dramatically increase for same-author/cross-topic and different-author/same-topic cases. The proposed Siamese network topologies, however, displayed a significantly higher discriminative power than the stylometric-feature-based systems. This is also evident in Fig. the neural features y using the trained feature extraction module of AdHominem for each of the documents. In a third step, we applied a t-SNE to reduce the dimension to produce a visual representation. Each color in the plot belongs to a different author. As it can be seen, the features y produced by AdHominem are well suited to discriminate between different unseen authors.