Using deep learning to analyse the times of the UN Security Council

Abstract

Abstract This article analyses how digital humanities scholarship can make use of recent advances in deep learning to analyse the temporal relations in an online textual archive. We use transfer learning as well as data augmentation techniques to investigate changes in United Nations Security Council resolutions. Instead of pre-defined periods, as it is common, we target the years directly. Such a text regression task is novel in the digital humanities as far as we can see and has the advantage of speaking directly to historical relations. We present not only very good experimental results but also demonstrate how such text regressions can be interpreted directly and with surrogate topic models.