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Authorship verification is the task of analyzing the linguistic patterns oftwo or more texts to determine whether they were written by the same author ornot. The analysis is traditionally performed by experts who consider linguisticfeatures, which include spelling mistakes, grammatical inconsistencies, andstylistics for example. Machine learning algorithms, on the other hand, can betrained to accomplish the same, but have traditionally relied on so-calledstylometric features. The disadvantage of such features is that theirreliability is greatly diminished for short and topically varied social mediatexts. In this interdisciplinary work, we propose a substantial extension of arecently published hierarchical Siamese neural network approach, with which itis feasible to learn neural features and to visualize the decision-makingprocess. For this purpose, a new large-scale corpus of short Amazon reviews fortext comparison research is compiled and we show that the Siamese networktopologies outperform state-of-the-art approaches that were built up onstylometric features. Our linguistic analysis of the internal attention weightsof the network shows that the proposed method is indeed able to latch on tosome traditional linguistic categories.

Explainable Authorship Verification in Social Media via Attention-based Similarity Learning