Using generative AI to investigate the multimodal representation of refugees and migrants

Abstract

This article uses AI-generated images to consider patterns in the way refugees and migrants are visually represented on the internet. AI text-to-image generation is treated as a diagnostic tool enabling the identification and analysis of recurring features in the visual representation of social groups. The theoretical departure point is the notion of multimodal constructions developed in cognitive linguistics. 144 images are generated by prompts instantiating a [refugees/migrants + motion verb] construction. The images are then analysed through the lens of Cognitive Critical Discourse Analysis to establish the visual correlates of these verbal forms and to reveal their ideological and (de)legitimating functions in reproducing stereotypes, stoking fear and ultimately sustaining power differentials. The results show that the image most likely to be entrenched as part of a multimodal construction is of a large mass of people moving on foot toward the viewer. The ideological implications of this and other patterns detected are discussed as are differences in the visual representations associated with people designated as ‘refugee’ versus ‘migrant’.