The goal of this project is twofold: First, it is to be considered a contribution to the emerging field of “distant viewing”, which uses quantitative methods to assess a corpus consisting of a large number of visual media. Currently, deep learning methods play a minor role in distant viewing, as most of the projects use pre-trained networks. This is understandable, as training is not trivial. However, using pre-trained networks significantly reduces the amount of possible research questions. Moreover, a better understanding of the training process allows us to contribute to the field of “critical machine learning” as well; specifically we try to point out some of the benefits and pitfalls of training an artificial neural network for a humanities research project.
Second, this project extends the methodology of studying cultural memory. By means of training a residual convolutional neural network in Facebook’s Detectron2 framework to recognize a number of national(istic) symbols from Eastern Europe, we show how Ukrainian nationalist Stepan Bandera (1909–1959) is instrumentalized in the online discourse about the recent Ukrainian crisis. From 2013 onwards we have collected a total of 800 YouTube video clips about Bandera. Our custom-trained network is then used to identify the respective national(istic) context of the videos in question.