Bridging ethnography and AI: a reciprocal methodology for studying visual political action

This article proposes a methodological approach to address the analytical challenge of meaningfully studying visual politics in the current abundance of online image data. It proposes a novel methodological process of bridging ethnography and computational methods for analysing visual data in a manner that avoids mixing and blurring the boundaries of the two methods while establishing a continuous interchange between them. The methodological development enables investigating how youth participate through image creation and usage, both online and offline, by outlining repertoires of visual political action. We argue that combining ethnographic research with supervised deep learning-based AI and pre-trained neural networks allows for systematically analysing large image datasets while maintaining interpretative, analytical perspectives and contextual sensitivity. Our results show that the existence of common visual features in images, the re-evaluation of the image categories and the creation of subcategories constitute key aspects for an ethnographically-informed image classification.